Saturday, December 7
ICDM Workshops (Center Tower, 37th floor)
 Majestic 1Majestic 2Majestic 3Majestic 4Majestic 5Majestic 6Majestic 7Majestic 8Majestic 9Majestic 10Majestic 11
10:00am-10:30amCoffee Break
12:00pm-1:30pmLunch (on your own)
3:00pm-3:30pmCoffee Break


Saturday, December 7
ICDM Workshops (Center Tower, 4th floor)
 Cityview 1Cityview 2Cityview 3Cityview 4Cityview 5Cityview 6Cityview 7Cityview 8
10:00am-10:30amCoffee Break
12:00pm-1:30pmLunch (on your own)
3:00pm-3:30pmCoffee Break




8th International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM)

Organizers: Raju Vatsuvai

Extracting useful geoinformation from large heterogeneous spatial and spatiotemporal datasets requires efficient and novel spatial and spatiotemporal data mining techniques. Traditional data mining techniques are ineffective as they do not model the idiosyncrasies of the spatial domain, which include (but are not limited to) spatial autocorrelation, spatial context, spatial heterogeneity, and spatial constraints. The SSTDM workshop seeks to bring together researchers from academia, government, and geospatial industry to facilitate cross-disciplinary exchange of ideas in the area of spatial and spatiotemporal data mining.

8:40 - 8:45Opening Remarks
8:45 - 9:30

Keynote Speaker
Dr. Nikunj Oza, NASA

9:30 - 10:00

The Passenger Demand Prediction Model on Bus Networks
Chunjie Zhou, Pengfei Dai, and Renpu Li

10:00 - 10:30Coffee break
10:30 - 11:00

4D-SNN: A Spatio-temporal Density-based Clustering Approach with 4D Similarity
Ricardo Oliveira, Maribel Yasmina Santos, and Joao Moura-Piresi

11:00 - 11:30 

New Spatiotemporal Clustering Algorithms and their Applications to Ozone Pollution
Sujing Wang, Tianxing Cai, and Christoph F. Eick

11:30 - 12:00

A Novel Approach to Trajectory Analysis Using String Matching and Clustering
Madhuri Debnath, Praveen Tripathi, and Ramez Elmasri

12:00 - 1:30Lunch
1:30 - 2:15

Keynote Speech: Implementing Gaussian spatial autoregressive models for massive georeferenced datasets: some spatial data mining outcomes
Prof. Daniel Griffith

2:15 - 2:45

Geographic Proximity of Friends in Gowalla
Tommy Nguyen, Mingming Chen, and Boleslaw Szymanski

2:45 - 3:30Coffee break and room rearragement
 Parallel Session 1
3:30 - 4:00

Multi-sensor Remote Sensing Image Change Detection: An Evaluation of Similarity Measures
Raju Vatsavai and Karthik Ganesan Pillai

4:00 - 4:30

Qualitative spatial structure in complex areal objects using location-free, mobile geosensor networks
Alan Both and Matt Duckhamzzz

4:30 - 5:00

Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework
David Gagne, Amy McGovern, Jerald Brotzge, and Ming Xue

5:00 - 5:30

DLOREAN: Dynamic LOcation-aware REconstruction of multiwAy Networks
Fredrik Johansson, Vinay Jethava, and Devdatt Dubhashi

 Parallel Session 2 (Temporal Data Mining)
3:30 - 4:00

Pimp my Segmentation - Fast Time Series Segmentation Algorithms Based on Update Techniques for Polynomial Approximations
Andri Gensler, Thiemo Gruber, and Bernhard Sickt

4:00 - 4:30

An Integer Programming Approach to Temporal Pattern Matching Queries
Megan Monroe and Amol Deshpande

4:30 - 5:00

Mining Semantic Time Period Similarity in Spatio-Temporal Climate Data
Michael McGuire and Ziying Tang

5:30Closing Remarkrs


The 8th Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM)

Organizers: Yong Shi, Chris Ding, Yingjie Tian, Zhiquan Qi

This workshop intends to promote the research interests in the connection of optimization and data mining as well as real-life applications among the growing data mining communities. It calls for papers to the researchers in the above interface fields for their participation in the conference. The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of optimization and data mining related topics.


8:30 - 10:00

Oral Presentations

Information-based Top-k Influential User Discovery in Social Networks
Guo Jing, Zhang Peng, and Zhou Chuan

The Class Imbalance Problem in Multi-instance Learning
Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz, and Stan Matwin

How to Improve the Quality of Pedestrian Detection Using the Priori Knowledge
Zhiquan Qi, Yingjie Tian, Xiaodan Yu, Yong Shi

Unsupervised Clustering Strategy Based on Label Propagation
Jiguang Liang, Xiaofei Zhou, Ying Sha, Ping Liu, and Li Guo
10:00 - 10:30Coffee break
10:30 - 11:30

Invited Talk: Sparse Learning for Big Data
Jieping Ye, Arizona State University

11:30 - 12:00 

Infinite Mixed Membership Matrix Factorization
Avneesh Saluja, Mahdi Pakdaman, Dongzhen Piao, and Ankur P.Parikh, 

12:00 - 1:30Lunch
1:30 - 2:30

Invited Talk: Incremental Optimization of Performance Measures
Zhi-Hua Zhou, Nanjing University

2:30 - 3:00

Optimal Correlation Clustering via MaxSAT
Jeremias Berg and Matti Jarvisalo

3:00 - 3:30Coffee break
3:30 - 4:50

Oral Presentations

Cost-Free Learning for Support Vector Machines with a Reject Option
Guibiao Xu

Robust Cost-Sensitive Confidence-Weighted Classification
Alnur Ali and Kevyn Collins-Thompson

Fast Spectral Clustering with Landmark-based Subspace Iteration
Zejun Gan, Chaofeng Sha, and Junyu Niu

Dissimilarity Features in Recommender Systems
Christos Zigkolis, Savvas Karagiannidis, and Athena Vakali


Incremental Clustering, Concept Drift and Novelty Detection (IclaNov)

Organizers: Pascal Cuxac, Jean-Charles Lamirel, Vincent Lemaire

This workshop aims to offer a meeting opportunity for academics and industry-related researchers, belonging to the various communities of Computational Intelligence, Machine Learning, Experimental Design and Data Mining to discuss new areas of incremental clustering, concept drift management and novelty detection and on their application to analysis of time varying information of various natures. Another important aim of the workshop is to bridge the gap between data acquisition or experimentation and model building.


9:00 - 9:10Opening
10:00 - 10:10

Incrementally Optimizing AUC
Zhi-Hua Zhou

10:10 - 10:40Coffee break
10:40 - 11:00

Unsupervised Learning for Analyzing the Dynamic Behavior of Online Banking Fraud
Guénaël Cabanes, Younès Bennani, and Nistor Grozavu

11:00 - 11:20

Analysis of UN Voting Patterns via Diffusion Geometry and Thematic Clustering
Minh-Tam Le, Matthew Lawlor, Bruce Russett, John Sweeney, and Steven Zucker

11:20 - 11:40

Network forensic analysis using growing hierarchical SOM
Shin-Ying Huang and Yennun Huang

11:40 - 12:00

Green vs. non-green customer behavior: A Self-Organizing Time Map over greenness
Annika H. Holmbom, Samuel Rönnqvist, Peter Sarlin, Tomas Eklund, and Barbro Back

12:00 - 1:30Lunch break
1:30 - 1:50

Ontological Hierarchical Clustering for Library-based Microbial Source Tracking
Anya Goodman, Aldrin Montana, Alex Dekhtyar, Michael Black, and Chris Kitts

1:50 - 2:10

Analysis of Incrementally Generated Clusters in Biological Networks Using Graph-Theoretic Filters and Ontology Enrichment
Sean West, Kathryn Dempsey, Sanjukta Bhowmick, and Hesham Ali

2:10 - 2:40

Dynamic Data Analytics with an Incremental Clustering Approach
Fernando Mendes, Maribel Yasmina Santos, and João Moura-Pires

2:40 - 3:00

A Predictive Coding Framework for Learning to Predict Changes in Streaming Data
Bonny Banerjee and Jayanta Dutta

3:00 - 3:30Coffee break
3:30 - 3:50

An Online Clustering Algorithm that Ignores Outliers: Application to Hierarchical Feature Learning from Sensory Data
Bonny Banerjee and Jayanta Dutta

3:50 - 4:10

Adaptive Budget for Online Learning
Talieh S. Tabatabaei, Fakhri Karray, and Mohamed S. Kamel

4:10 - 4:30

Auto-tuning Kernel Mean Matching
Yun-Qian Miao, Ahmed Farahat, and Mohamed Kamel

4:30 - 4:50

Incremental Anomaly Detection in Graphs
William Eberle and Lawrence Holder

4:50 - 5:10Close


The 1st International Workshop on High Dimensional Data Mining (HDM)

Organizers: Ata Kaban

Unprecedented technological advances lead to increasingly high dimensional data sets in all areas of science, engineering and businesses. The number of features in such data is often of the order of thousands or millions -- that is much larger than the available sample size. Geometric intuition breaks down, statistical estimation becomes problematic, classical data analysis methods become inadequate, questionable, or inefficient at best. This workshop aims to promote new advances and research directions to address the curses, and to uncover the blessings of high dimensionality in data mining.


8:45 - 9:00

Opening: Welcome & Introduction
Ata Kaban

9:00 - 10:00

Invited Talk: High dimensional data case study: fMRI MVPA
Jo Etzel

10:00 - 10:30Coffee break
10:30 - 12:00

Invited Talk: The Unreasonable Effectiveness of Random Projections in Computer Science
Bob Durrant

12:00 - 1:30Lunch break
1:30 - 2:30

Invited Talk: Advanced Subspace Clustering Techniques
Stephan Gunnemann

2:30 - 3:00

Pattern Discovery in High Dimensional Binary Data
Peng Jiang and Michael T Heath

3:00 - 3:30Coffee break
3:30 - 4:00

Dimensionality, Discriminability, Density & Distance Distributions
Michael E. Houle

4:00 - 4:30

Can Shared Nearest Neighbors Reduce Hubness in High-Dimensional Spaces?
Arthur Flexer and Dominik Schnitzer

4:30 - 5:00

Transform Residual K-means Trees for Scalable Clustering
Jiangbo Yuan and Xiuwen Liu

5:00 - 5:30

A New Look at Compressed Ordinary Least Squares
Ata Kaban

5:30 - 6:00Discussion & Closing


The First IEEE ICDM Workshop on Causal Discovery 2013 (CD2013)

Organizers: Jiuyong Li, Kun Zhang, Jian Pei, Lin Liu

Traditionally, causal relationships are identified based on controlled experiments. However, conducting such experiments is impossible in many cases due to cost or ethical concerns. Therefore there has been an increasing interest in discovering causal relationships based on observational data only. This workshop aims at bringing together researchers and practitioners with the interest in causal discovery, from data mining and other disciplines, to communicate their new ideas, algorithms, and novel applications of causal discovery methods.


8:30 - 8:45Opening and Welcome
8:45 - 9:10

A Novel Structural AR Modeling Approach for a Continuous Time Linear Markov System
Marina Demeshko, Takashi Washio, and Yoshinobu Kawahara

9:10 - 9:35

Prior Knowledge Driven Causality Analysis in Gene Regulatory Network Discovery
Shun Yao, Shinjae Yoo, and Dantong Yu

9:35 - 10:00

On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example
Kun Zhang, Zhikun Wang, and Bernhard Schölkopf

10:00 - 10:30Coffee break
10:30 - 11:30

Clark Glymour, Alumni University Professor, Carnegie Mellon University

11:30 - 11:55

Mining Causal Association Rules
Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, and Bingyu Sun

11:55 - 12:20

Support Vector Machines for Uplift Modeling
Łukasz Zaniewicz and Szymon Jaroszewicz


4th ICDM International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms (KD-Cloud)

Organizers: Raju Vatsuvai

KDCloud workshop intends to bring together researchers, developers, and practitioners from academia, government, and industry to discuss new and emerging trends in cloud computing technologies, programming models, and software services and outline the data mining and knowledge discovery approaches that can efficiently exploit this modern computing infrastructure.

8:40 - 8:45Opening Remarks
8:45 - 9:30

Keynote Speech
Professor Varun Chandola, SUNY-Buffalo

9:30 - 10:00

Cloud Based Predictive Analytics
Klavdiya Hammond and Aparna Varde

10:00 - 10:30Break
10:30 - 11:00

Accelerating Frequent Itemsets Mining on the Cloud: A MapReduce-Based Approach
Zahra Farzanyar

11:00 - 11:30

Decision Support in Data Center Sustainability
Michael Pawlish, Aparna S. Varde, and Stefan Robila

11:30 - 12:00


Decentralized K-means using randomized Gossip protocols for clustering large datasets
Jerome Fellus, David Picard, and Philippe-Henri Gosselin

12:00Closing Remarks


Biological Data Mining and its Applications in Healthcare (BioDM)

Organizers: Li Xiaoli, See-Kiong Ng, Jason T.L. WangAndrea BertottiAlessandro Fiori

Biologists are stepping up their efforts to understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data. To exploit these biomedical data for discovering new knowledge that can be translated into clinical applications, there are fundamental difficulties that have to be overcome. This workshop will disseminate the best data mining approaches to address the challenging issues in various biomedical data analysis.


9:00 - 9:10

Welcoming and introduction
Xiao-Li Li

9:10 - 10:00

Invited Talk 1: Biomarker Selection using Sparse Coding
Chris H.Q. Ding, University of Texas at Arlington

10:00 - 10:30Coffee break
 Morning Session: Disease gene detection, sequencing data analytics
10:30 - 10:55

Finding Discriminatory Genes: a methodology for validating microarray studies
Sheehan Khan and Russ Greiner

10:55 - 11:20

Constrained Gaussian Process Regression for Gene-Disease Assocation
Oluwasanmi Koyejo, Cheng Lee, Joydeep Ghosh

11:20 - 11:45

A Tolerance Graph Approach for Domain-Specific Assembly of Next Generation Sequencing Data
Julia Warnke and Heshan Ali

11:45 - 12:10

A Biclustering Algorithm to Discover Functional Modules from ENCODE ChIP-seq Data
Chao Wu, Arjun Bakshi, Bruce Aronow, Anil Jegga, Raj Bhatnagar

12:10 - 1:45Lunch break
 Afternoon Session: Social Network and Text Mining for Healthcare
1:45 - 2:10

A New Framework for Distilling Higher Quality Information from Health Data via Social Network Analysis
Miriam Baglioni, Stefania Pieroni, Filippo Geraci, Fabio Marian, Sabrina Molinaro, Marco Pellegrin, and Ernesto Lastres

2:10 - 2:35

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes
Gunnar R

2:35 - 3:00

An Improved Model for Depression Detection in Micro-blog Social Network
Xinyu Wang, Chunhong Zhang, Li Sun

3:00 - 3:30Coffee break


The Third International Workshop on Data Mining in Networks (DaMNet)

Organizers: Giuseppe Di Fatta, Antonio Liotta

The complexity of numerous social, biological, and communication systems is driving many researchers towards the adoption of data mining approaches for the analysis and control of complex networks. The workshop focus will encompass data mining algorithms and applications for communication networks, such as peer-to-peer systems, mobile ad-hoc networks, wireless sensor networks, the World Wide Web, and other complex networks, such as social networks, metabolic networks, protein-protein interaction networks and citation networks.


8:30 - 8:35
Welcome and opening remarks
Assoc. Prof. Giuseppe Di Fatta
8:35 - 9:35
Invited speaker: "Mining Propagation Data in Social Networks"
Francesco Bonchi, Yahoo! Research
9:35 - 10:00
Talk #1: "Scalable Flow-Based Community Detection for Large-Scale Network Analysis"
Seung-Hee Bae, Daniel Halperin, Jevin West, Martin Rosvall, and Bill Howe
10:00 - 10:30
Coffee break
10:30 - 10:55
Talk #2: "On Mining Biological Signals using Correlation Networks"
Kathryn Dempsey, Ishwor Thapa, Claudia Cortes, Zach Eriksen, Dhundy Bastola, and Hesham Ali
10:55 - 11:20
Talk #3:"On Identifying and Analyzing Significant Nodes in Protein-Protein Interaction Networks"
Rohan Khazanchi, Kathryn Dempsey, Ishwor Thapa, and Hesham Ali
11:20 - 11:45
Talk #4: "Evaluation and Comparison of Classification Techniques for Network Intrusion Detection"
Sait Murat Giray and Aydin Göze Polat
12:00 - 1:30
Lunch break
1:35 - 2:00
Talk #5: "Online Extreme Learning on Fixed-point Sensor Networks"
Hedde Bosman, Antonio Liotta, Giovanni Iacca, and Heinrich Wörtche
2:00 - 2:25
Talk #6: "Continuous monitoring of a computer network using multivariate adaptive estimation"
Dean Bodenham and Niall Adams
2:25 - 2:50
Talk #7: "Non-Euclidean Internet Coordinates Embedding"
Alexander Allan, Ross Humphrey, and Giuseppe Di Fatta
3:00 - 3:30
Coffee break
3:35 - 4:00
Talk #8: "Fast Algorithm for Approximate k-Nearest Neighbor Graph Construction"
Dilin Wang, Lei Shi, and Jianwen Cao
4:00 - 4:05
Closing remarks
Assoc. Prof. Giuseppe Di Fatta



Designing the Market of Data - for Synthesizing Data in Sciences and Businesses (MoDAT)

Organizers: Yukio Ohsawa, Akinori Abe

We discuss how to create the market where data are sold, opened free, or shared on negotiation determining reasonable conditions for sharing. Furthermore, we aim to create an environment where similarities between latent dynamics behind datasets are visualized for aiding the analogical matching of data scientists' knowledge, so that they can to learn techniques from others. Relevant areas include but not restricted to data/text mining and visualization, knowledge representation, and creative communication.


8:30 - 8:40

Opening Remarks: Who Can Live Without MoDAT ? The Market of Data for Synthesizing Data in Sciences and Businesses
Yukio Ohsawa, School of Engineering, The University of Tokyo

8:40 - 9:05

Data Marketplace for Efficient Data Placement
Hiroshi Maruyama, Daisuke Okanohara, and Shohei Hido, The Institute of Statistical Mathematics

9:05 - 9:30

Valuation of Data through Use Scenarios in Innovators’ Marketplace on Data Jackets
Chang Liu, Yukio Ohsawa, and Yoshitaka Suda, School of Engieering, The University of Tokyo

9:30 - 9:55

Curating and mining (big) data
Akinori Abe, Chiba University

10:00 - 10:30Coffee break
10:30 - 10:55

Quantifying and Recommending Expertise When New Skills Emerge
Dongping Fang, Kush Varshney, Jun Wang, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic, and John Bauer, IBM Thomas J Watson Research Center

10:55 - 11:20

Acquisition of Text-Mining Skills for Beginners Using TETDM
Rina Nakagochi, Kayo Kawamoto, and Wataru Sunayama, Graduate School of Information Sciences, Hiroshima City University

11:20 - 11:45

Spatiotemporal Life-log Mining of Wheelchair Users’ Driving for Visualizing Accessibility of Roads
Yusuke Iwasawa and Ikuko E. Yairi, Sophia University

11:45 - 1:30Short discussion and lunch break
1:30 - 2:10

Invited Presentation: Valuation of Partly Disclosed Datasets for Prediction
Hiroe Tsukabi, The Institute of Statistical Mathematics

2:10 - 2:35

IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events
Chen Zhang and Hao Wang, Institute of Software, Chinese Academy of Sciences

2:35 - 3:00

Detecting topics from Twitter posts during TV program viewing
Takanobu Nakahara and Yukinobu Hamuro, Institute of Business and Accounting, Kwansei Gakuin University

3:00 - 3:30Coffee break
3:30 - 3:55

A Method for Generating Ontologies in Requirements Domain for Searching Data Sets in Marketplace
Noriyuki Kushiro, Kyushu Institute of Techology

3:55 - 4:20

Frame as a clue to intention of data: toward new product ideas with framed components
Jun Nakamura and Masahiko Teramoto, Volvo Group Trucks, Sales & Marketing APAC and JVs

4:20 - 4:45

e-Trucks realize four Zeros expectations -The Challenge by Market of Data-
Masahiko Teramoto and Jun Nakamura, Volvo Group Trucks & Technology,Advanced Technology & Research

4:50 - 6:00

Play Innovators Marketplace on Data Jackets, toward Future Activities of MoDAT
Yoshitaka Suda, The University of Tokyo and CREST, JST.


4th IEEE Workshop on Privacy Aspects of Data Mining (PADM 2013)

Organizers: Aris Gkoulalas-Divanis

Vast amounts of data need to be collected, analyzed, and shared in a privacy-preserving way, in a wide spectrum of applications related to social networks, healthcare, advertising, and cloud computing. This introduces significant research challenges that are associated to the type and intended use of data, while different applications have often unique privacy requirements. The PADM workshop focuses on principled privacy research that aims to address these challenges from a theoretical and a practical perspective.


8:45 - 9:00Opening Session
9:00 -10:00

Invited Talk: Adaptive differentially-private data release for data sharing and data mining
Li Xiong

10:00 - 10:30Coffee break
10:30 - 10:55

Session 1: Privacy models and algorithms for emerging applications

Differentially Private Anomaly Detection with a Case Study on Epidemic Outbreak Detection
Liyue Fan and Li Xiong

A Semi-Supervised Learning Approach To Differential Privacy
Geetha Jagannathan, Claire Monteleoni, and Krishnan Pillaipakkamnatt

Select-Organize-Anonymize: A framework for trajectory data anonymization
Giorgos Poulis, Spiros Skiadopoulos, Grigorios Loukides, and Aris Gkoulalas-Divanis

12:00 - 2:00Lunch break
2:00 - 3:00

Invited Talk: Incentive-compatible privacy-preserving distributed data mining
Murat Kantarcioglu

3:00 - 3:30Coffee break
3:30 - 4:30

Session 2: Theory and methods for discrimination-aware data mining

Data anonymity meets non-discrimination
Salvatore Ruggieri

The Independence of Fairness-Aware Classifiers
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma

4:30 - 5:30

Session 3: Techniques for privacy-preserving data mining

Privacy-Preserving Kernel k-Means Outsourcing with Randomized Kernels
Keng-Pei Lin

Privacy Preserving Social Network Publication Against Mutual Friend Attacks
Chongjing Sun, Philip S Yu, Xiangnan Kong, and Yan Fu

5:30 - 6:00Discussion and Concluding Remarks


Fifth Workshop on Data Mining Case Studies and Practice Prize (DMCS-5)

Organizers: Gabor Melli, Brendan Kitts

The Data Mining Case Studies Workshop and Practice Prize were established in 2005 to showcase the very best in data mining case deployments. Data Mining Case Studies continues into the fifth workshop and prize competition with ICDM-2013. Data Mining Case Studies will highlight data mining implementations that have been responsible for a significant and measurable improvement in business operations, or an equally important scientific discovery, or some other benefit to humanity.

8:30 - 8:50Breakfast and Coffee
8:50 - 9:00Welcome!  Opening remarks and preview
9:00 - 10:00

The Future of Advertising
Brian Burdick (former Chief Technology Officer SpecificMedia)

10:00 - 10:30Morning Tea
10:30 - 10:50

The Making of A Large-Scale Online Ad System: Practical Lessons Building one of the World’s Largest Online Ad Servers
Brendan Kitts

10:50 - 11:10

"Engine matters": a data driven study on cyclists’ performance
Paolo Cintia, Luca Pappalardo, and Dino Pedreschi

11:10 - 11:30

Monetization and Services on a Real Online Social Network Using Social Network Analysis
Blaise Ngonmang, Emmanuel Viennet, Savaneary Sean, Philippe Stepniewski, Françoise Fogelman-Soulié, and Rémi Kirche

11:30 - 11:45

Synthesis of Decision Making: From Data to Business Execution
Raul Pavon and Beth Carpenter

11:45 - 12:00

A Distinguishing Attack with a Neural Network
William de Souza and Allan Tomlinson

12:00 - 1:30Lunch break
1:30 - 1:50

Television Ad Targeting
Brendan Kitts

1:50 - 2:10

A Novel and Successful Credit Card Fraud Detection System Implemented in a Turkish Bank
Ekrem Duman, Ayse Buyukkaya, and Ilker Elikucuk

2:10 - 2:25

Improving every child’s chance in life
Inna Kolyshkina, Marcus Brownlow, and Jarrad Taylor

2:25 - 3:00

Automatic Detection of Sub-Kilometer Craters in High Resolution NASA Planetary Images 
Wei Ding

3:00 - 3:30Coffee break
3:30 - 4:00

Automatically Insert Affiliated Links and Lift Your Revenue!
Gabor Melli

4:00 - 5:00

Association Mining with Applications
Geoff Webb

5:00 - 5:102013 DMCS Practice Prize Winners
5:10 - 5:50Adjourn, freshen up for dinner
5:50 - 6:00Meet up at Hotel lobby; walk together to the dinner venue
6:00Dinner, drinks, and relaxation


The Fourth Workshop on Data Mining for Service (DMS)

Organizers: Shusaku Tsumoto

The workshop is aimed at bringing together researchers from the areas of the service sector and data mining. We expect to encourage an exchange of ideas and perceptions through the workshop, focused on service and data mining. The focus of this workshop is on empirical findings, methodological papers, and theoretical and conceptual insights related to data mining in the field of various service application areas.


9:00 - 9:10

Welcoming and Introduction
Shusaku Tsumoto and Katsutoshi Yada

9:10 - 9:35

Evaluation of Discount Effect Using Poisson Regression Based on Interaction Effect Between Bargain Scale and Product Category
Natsuki Sano and Tomomichi Suzuki

9:35 - 10:00 

Using Eye-Tracking Data of Advertisement Viewing Behavior to Predict Customer Churn
Michel Ballings and Dirk Van den Poel

10:00 - 10:30Coffee Break
10:30 - 10:55

Application of Bayesian Network Sheds Light on Purchase Decision Process basing on RFID Technology
Yi Zuo and Katsutoshi Yada

10:55 - 11:20

Analytics-Based Solutions for Improving Alert Management Service for Enterprise Systems
Maitreya Natu, Vaishali Sadaphal, Anuja Kelkar, Utkarsh Naiknaware, Sachin Sukhlecha and Ashish Sanadhya

11:20 - 1:30Lunch
1:30 - 3:00

Invited Report: Visual Representations Enhancing Insight
Kazuo Misue

3:00 - 3:30Coffee break
3:30 - 3:55

Understanding Service Quality and Customer Churn by Process Discovery for a Multi-National Banking Contact Center
Edward M.L. Peters, Guido Dedene and Jonas Poelmans

3:55 - 4:20

Prescriptive Analytics for Allocating Sales Teams to Opportunities
Ban Kawas, Mark Squillante, Dharmashankar Subramanian and Kush Varshney

4:20 - 4:45

Clustering of order sequences based on the typicalness index for finding clinical pathway candidate
Shoji Hirano and Shusaku Tsumoto

4:45 - 5:15Discussion
5:15 - 5:30

Shusaku Tsumoto and Katsutoshi Yada


The Second International Workshop on Experimental Economics and Machine Learning (EEML 2013)

Organizers: Rustam Tagiew, Dmitry Ignatov, Fadi Amroush

This workshop's intention is to integrate scientists from Experimental Economics with those from AI & Data Mining. In Experimental Economics, laboratory and field experiments are conducted on subjects in order to improve theoretical knowledge about human strategic interactions. Although subjects' preferences are restricted in experiments, the exclusive application of game theory does not suffice to explain the recorded data. It requires the methods of machine learning - development and evaluation of sophisticated models.


9:00 - 10:00

Keynote Talk
Edward M.L. Peters

10:00 - 10:30Coffee break
11:00 - 12:00

EEML Session 1 (Chair: Rustam Tagiew)

Zombies Walk Among Us: Cross-platform data mining for event monitoring
Emma L. Tonkin and Heather D. Pfeiffer

Empirical Discovery of Potential Value Leaks in Processes by means of Formal Concept Analysis
Edward M.L. Peters, Guido Dedene, and Jonas Poelmans

12:00 - 2:00Lunch
2:00 - 3:00

EEML Session 2 (Chair: Dmitry Ignatov) 

Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis
Alexander Porshnev, Ilya Redkin, and Alexey Shevchenko

Social Learning in Networks: Extraction of Deterministic Rules
Rustam Tagiew, Dmitry Ignatov, and Fadi Amroush

3:00 - 3:30Coffee break
3:30 - 4:30

EEML Session 3 (Chair: Rustam Tagiew) 

The Early Booking Effect and Other Determinants of Hotel Room Prices in Europe
Anastasia Bezzubtseva

Information Fusion Concepts and Implementation based on IGIS technologies in Naval-Oriented Decision Making Support System
Natalia Zhukova


2013 International Workshop on Domain Driven Data Mining (DDDM)

Organizers: Frank Jiang

The 2013 International Workshop on Domain Driven Data Mining (DDDM2013) workshop aims to provide a premier forum for sharing findings, knowledge, insight, experience and lessons in tackling potential challenges in discovering actionable knowledge from complex domain problems, promoting interaction and filling the gap between academia and business, and driving a paradigm shift from interesting hidden pattern mining to actionable knowledge discovery in varying data mining domains.

9:00 - 9:05

Opening Address
Workshop Co-Chairs

9:05 - 10:00

Keynote Speech: Big Data Analytics in Mobile Environments
Professor Hui Xiong

10:00 - 10:30Coffee break
10:30 - 12:00

Session 1:

Characterization of Corpora from Enterprise Technology Creation for Retrieval and Mining
Vinay Deolalikar

Towards Understanding the Effectiveness of Election Related Images in Social Media
Junhuan Zhu, Jiebo Luo, Quanzeng You, and John R. Smith

Predicting Current User Intent with Contextual Markov Models
Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy, and Toon Calders

12:00 - 1:30Lunch break
1:30 - 2:15

Keynote Speech: Learning Domain-Knowledge to Better Learn Online Behaviors
Dr. Gabor Melli

2:15 - 2:45

Session 2:

Weighted Task Regularization for Multitask Learning
Yintao Liu, Anqi Wu, Dong Guo, Ke-Thia Yao, and Cauligi Raghavendra

2:45 - 3:30Coffee break
3:30 - 5:40

Parallel Session II: 

Detection of Precursors to Aviation Safety Incidents Due to Human Factors
Igor Melnyk, Pranjul Yadav, Michael Steinbach, Jaideep Srivastava, Vipin Kumar, and Arindam Banerjee

Label Distribution Learning
Xin Geng and Rongzi Ji

Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features
Michael Kamp, Mario Boley, and Thomas Gärtner

3:30 - 5:30

Parallel Session III: 

Towards the integration of Constrained Mining with Star Schemas
Andreia Silva and Cláudia Antunes

Mining Correlation Patterns among Appliances in Smart Environment
Yi-Cheng Chen, Chien-Chih Chen, Wen-Chih Peng, and Wang-Chien Lee

Model Driven Engineering for data miners simulation
Jose Evora-Gomez
5:40 - 6:00

Closing Remarks
Workshop Co-Chairs


Mining and Understanding from Big Data (BigMUD)

Organizers: Xueqi Cheng, Charles Ling, Fei Wang, Alvin Chin, Jilei Tian

Complex, heterogeneous and dynamic big data are generated from Internet, Web, social media, retail, finance, healthcare, to name a few. Due to the massive volume and inherent complexity, it is extremely difficult to gather?store, aggregate, manage and analyze big data and finally mine valuable knowledge from the raw data. The workshop on Mining and Understanding from Big Data will provide the scientific and industry community a dedicated forum for discussing state-of-the-art research on big data analysis, mining, understanding, learning, as well as the data management architecture and visualization of very large data sets. The BigMUD2013 workshop will be an excellent forum to help the community discuss the current state and the future challenges of Big Data theories and applications.


 Session 1: Welcome, Invited Talk
1:30 - 1:35

BigMUD Chairs

1:35 - 2:30

SAE: Social Analytic Engine for Large-scale Networks (Invited Talk)
Jie Tang

 Session 2: Papers
2:30 - 3:00

Towards optimal symbolization for time series comparisons
Gavin Smith, James Goulding, and Duncan Barrack

3:00 - 3:30

In-Core Computation of Geometric Centralities with HyperBall: A Hundred Billion Nodes and Beyond
Paolo Boldi and Sebastiano Vigna

3:30 - 4:00Coffee break
 Session 3: Papers
4:00 - 4:30

A Study on Privacy Preservation for Multi-user and Multi-granularity
Xianmang He

4:30 - 5:00

Scalable Audience Reach Estimation in Real-time Online Advertising
Ali Jalali, Santanu Kolay, Peter Foldes, and Ali Dasdan

5:00 - 5:30

GPU-Accelerated Query by Humming Using Modified SPRING Algorithm
Guangchao Yao, Yao Zheng, Limin Xiao, Li Ruan, Yongnan Li, and Zhenzhong Zhang

5:30 - 5:55Discussion
5:55 - 6:00Closing remarks


Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE)

Organizers: Erik Cambria, Ping Chen

SENTIRE aims to provide an international forum for researchers in the field of opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of the workshop comprehends Web mining, AI, Semantic Web, information retrieval and natural language processing.


8:30 - 8:45

Welcoming and introduction
Erik Cambria

8:45 - 9:30

Keynote Speech
Yang Liu

9:30 - 10:00

Multi-class sentiment analysis with clustering and score representation
Mohsen Farhadloo

10:00 - 10:30Coffee break
10:30 - 11:00

Interest analysis using semantic PageRank and social interaction content
Chung-Chi Huang

11:00 - 11:30

Learning the roles of directional expressions and domain concepts in financial news analysis
Pekka Malo

11:30 - 12:00

Robust language learning via efficient budgeted online algorithms
Simone Filice

12:00 - 1:30Lunch break
1:45 - 2:00

Possible usage of sentiment analysis for calculating vectors of felific calculus
Rafal Rzepka

2:00 - 2:15

Interpreting or describing? Verb abstraction in the linguistic category model
Aleksander Wawer

2:15 - 2:30

Sentiment analysis in news articles using sentic computing
Prashant Raina

2:30 - 2:45

Enhancing sentiment classification performance using bi-tagged phrases
Basant Agarwal

2:45 - 3:00

A framework of review analysis for enhancement of business decision making
Atika Qazi

3:00 - 3:30Coffee break
3:30 - 4:00

Joint and pipeline probabilistic models for fine-grained sentiment analysis:
Extracting aspects, subjective phrases and their relations

Roman Klinger

4:00 - 4:30

Dynamic construction of dictionaries for sentiment classification
Ameur Hanen

4:30 - 5:00

Subjective Bayes method for word semantic similarity measurement
Junhua Wang

5:00 - 5:30

Pattern enhanced topic models for information filtering
Yang Gao

5:30 - 6:00

Concluding remarks
Erik Cambria


Astroinformatics (AstroInfo)

Organizers: Ricardo Vilalta, G. Jogesh Babu, Kirk D Borne

Astroinformatics is an interdisciplinary field of science that applies modern computational tools to the solution of astronomical problems. Data repositories have gone from gigabytes into terabytes, and we expect those repositories to reach the terabytes in the coming years. An important area in astroinformatics is the application of data mining tools for analysis of large astronomical repositories and surveys. Important topics include data description, astronomical classification, taxonomies, data mining, machine learning, visualization, and astrostatistics.


8:30 - 8:50Social Gathering
8:50 - 9:00Welcome and Introduction
9:00 - 9:30

Invited Talk: Calculation and Applications of Bayesian Evidence in Astrophysics and Particle Physics Phenomenology.
Farhan Feroz

9:30 - 10:00 

Online Classification for Time-Domain Astronomy
Kitty Lo, Umaa Rebbapragada, Tara Murphy, and Kiri Wagstaff.

10:00 - 10:30Coffee Break
10:30 - 11:00

On Using SIFT Descriptors for Image Parameter Evaluation
Patrick McInerney, Juan Banda, and Rafal Angryk

11:00 - 11:30

Automatic Identification of Hexagonal Pattern Artifacts in Radio Astronomical Surveys
Dina Said, Jeroen Stil, Russ Taylor, and Ken Barker

11:30 - 12:00

Neural Networks for Astronomical Data Analysis and Bayesian Inference
Philip Graff, Farhan Feroz, Michael Hobson, and Anthony Lasenby

12:00 - 1:30Lunch
1:30 - 2:00

Region-based Querying Using Descriptor Signatures for Solar Physics
Juan Banda, Chang Liu, and Rafal Angryk

2:00 - 2:30

Invited Talk: Solar Data Mining
Rafal Angryk

2:30 - 3:00

Panel Discussion Session

3:00 - 3:30Coffee break
3:30End of Workshop


Data Mining in Biomedical Informatics and Healthcare (DMBIH)

Organizers: Carlo BarbieriCynthia BrandtSamah Jamal FodehChristopher Gillies, José D. Martin-GuerreroDaniela Stan RaicuMohammad-Reza Siadat

The ICDM'13 Data Mining in Biomedical Informatics and Healthcare workshop aims to provide a forum for data miners, informacists and clinical researchers to share information on their latest investigations in applying data mining techniques to biomedical and healthcare data. The broader context of the workshop comprehends artificial intelligence, information retrieval, machine learning, and natural language processing. Submissions are invited to address the need for developing new methods to mine, summarize and integrate the huge volume and diverse modalities of the structured and unstructured biomedical and healthcare data that can potentially lead to significant advances in the field.

1:30 - 1:40

Opening Remarks and Introductions
Daniela Raicu, Carlo Barbieri, Samah Fodeh

 Session I - Chair: Samah Fodeh (Yale)
1:40 - 2:35

Keynote: Data-Driven Approaches for Functional Biology
Hong Yu

2:35 - 3:00

Modeling the Effects of Microgravity On Oxidation in Mitochondria: A Protein Damage
Oliver Bonham-Carter, Jay Pedersen, Lotfollah Najjar, Dhundy Bastola

3:00 - 3:30Coffee break
 Session II - Chair: Carlo Barbieri (Fresenius)
3:30 - 3:55

Simulated Annealing Partitioning: An Algorithm for Optimizing Grouping in Cancer Data
Ran Qi, Shujia Zhou 

3:55 - 4:20

Mining Approximate Temporal Functional Dependencies Based on Pure Temporal Grouping
Carlo Combi, Paolo Parise, Pietro Sala, Giuseppe Pozzi

4:20 - 4:45

Reducing Classification Cost through Strategic Annotation Assignment
Daniela Raicu, Jose Zamacona, Alexander Rasin, Jacob Furst

4:45 - 5:00Coffee break
 Session III - Chair: Daniela Raicu (DePaul)
5:00 - 5:25

A Framework for High Level Conceptualization of Medical Notes: PTSD Case Study
Samah Fodeh, Mryan Zirkle, Ruth Reeves, Finch Dezon, Joseph Erdos, Cynthia Brandt

5:25 - 5:50

Handling Class Overlap and Imbalance to Detect Prompt Situations in Smart Homes
Barnan Das, Narayanan C. Krishnan, Diane Cook

5:50 - 6:00

Best Paper Award & Closing Remarks
Daniela Raicu, Carlo Barbieri, Samah Fodeh

Recommender Systems for Social Networks (RESSON) Canceled

Organizers: Luiz Pizzato, Panagiotis Papapetrou, Myra Spiliopoulou

Recommenders are a flagship application within the area of knowledge discovery. At the same time, social networking in the Web 2.0 involves gigabytes of data that can be mined to make recommendations while they allow for developing new insights into people's behavior; such insights can be used for improving the quality of the recommendations. The goal of this Workshop is to highlight the challenges in the exploitation of social information in recommender systems.

The 1st International Workshop on Mining Social Data at Work (1st MSDW) Merged with MoDAT

Organizers: Ana Paula Appel, Estevam R. Hruschka Jr., Rog▒rio de Paula, Leman Akoglu

MSDW aims to present, exam, and discuss empirical findings, methodologies and techniques, and theoretical and conceptual insights on mining social data by organizations for attaining internal and external knowledge. We cover a wide range of issues from attempts to bring social or other networking systems in large, traditional organizations, to recent developments in the use of social media within and across organizations, to the emergence of social businesses and their values to both employees and clients.

Ensemble Methods for Clustering and Co-Clustering (EMClust) Canceled

Organizers: Blaise Hanczar

In the last ten years, several works have shown that the ensemble approach could be useful for unsupervised learning. Now, the ensemble approach Clustering ensembles has been known as an effective method to improve the robustness and stability of clustering analysis. A wide variety of procedures have been proposed for these two step based on diverse theories. This workshop provides a forum to discuss related topics regarding the ensemble clustering and its applications.

1st Workshop on Biological Data mining and Database Applications (BiDaDa) Merged with BioDM

Organizers: Andrea BertottiAlessandro Fiori

In the post-genomic era, a huge amount of complex molecular data are generated with high throughput. Due to their complexity and heterogeneity, database and data mining application will play an increasingly crucial role in furthering biological research. The mission of this workshop is to disseminate research results and best practices of data mining approaches and database applications to cross-disciplinary researchers and practitioners from both the database/data mining and the life sciences areas.

First International Workshop on TeraScale In-Core Data Mining (TIDM) Canceled

Organizers: Koji TsudaRajeev RamanShin▒ichi Minato

Current approaches for mining big data such as streaming have fundamental limitations on the kind of mining that can be performed. However, machines with hundreds of GB of RAM are available nowadays. This workshop will focus on novel data mining algorithms that use RAM effectively by mining data stored in compact or compressed formats. A key underlying theme will be the application of succinct data structures including FM-index, wavelet trees, compressed suffix arrays, XBW etc.

IEEE ICDM Workshop on Mining Performance Patterns in Elite Sports (IEEE ICDM MPPES) Canceled

Organizers: Bahadorreza OfoghiJohn Zeleznikow

Mining Performance Patterns in Elite Sports (MPPES) is the IEEE ICDM workshop on using advanced data analytics techniques for decision making in the elite sports domain. This workshop intends to provide a forum for researchers in the different fields of Machine Learning, Statistics, Data Mining, and Sport Science to discuss related topics regarding the applications, current challenges, and possibly the future of using advanced data analytics techniques in knowledge discovery and knowledge generation in elite sports.

Data Mining for Geoinformatics and Environmental Hazards (DMG-EH) Canceled

Organizers: Guido CervoneJessica LinNigel Waters

Recent advancements in remote sensing, sensor networks, and social media provide an unprecedented access to geographical data. Mining these sources of massive "big data" creates new scientific challenges and opportunities. This workshop builds synergistic activities for the development of data mining methodologies for geoinformatics across complementary disciplines, with a focus on environmental hazards. Topics of interest include algorithms for detecting, observing, analyzing, forecasting, and visualizing various phenomena concerning environmental hazards.

The First Workshop on Internet Advertising Using Sentiment Analysis (AdSent 2013) Canceled

Organizers: Amitava DasDipankar DasErik CambriaMalamurali A R

Several communities from Sentiment Analysis and Internet Advertising domains have engaged themselves to conduct relevant conferences/workshops/symposiums in their respective fields. The particular goal of this workshop is to establish a knowledge bridge between these two communities i.e. Ad Scientists and Sentiment Analysis researchers, and to discuss future directions and challenges in research and development. We expect the workshop to help develop a new multidisciplinary community of researchers who are interested in these areas, and yield future collaboration and exchanges.