icdm 2004programmeFINAL by HC120830224420


									Monday November 1st 2004

       Tutorials                                          Workshops
0900 Gresham Fécamp          0900 Paganini Regency           Syndicate
  - Room     Room              - Ballroom Room               Room 4
                                                   W3 *
1030 T1      T2              1030 W1       W2                W5
1030                         1030
  -                            -                        Refreshment Break
1100                         1100
1100 Gresham Fécamp          1100 Paganini Regency           Syndicate
  - Room     Room              - Ballroom Room               Room 4
1230 T1      T2              1200 W1       W2                W5
1230                         1200
  -     Lunch Break            -                            Lunch Break
1400                         1330
1400 Gresham Fécamp          1330 Paganini Regency           Syndicate              Syndicate
  - Room     Room              - Ballroom Room               Room 4                 Room 6
1530 T3      T4              1530 W1       W2                W6                     W7
1530                         1530
  -                            -                        Refreshment Break
1600                         1600
1600 Gresham Fécamp          1600 Paganini Regency           Syndicate              Syndicate
  - Room     Room              - Ballroom Room               Room 4                 Room 6
                                                   W4 **
1730 T3      T4              1700 W1       W2                W6                     W7 **

T1: 10 Data Mining Mistakes - and How to Avoid Them
T2: Data Mining In Time Series Databases.
T3: Algorithmic Excursions in Data Streams.
T4: Data Grid Management Systems.

W1: Life Sciences Data Mining (full day)
W2: Foundations of Data Mining (full day)
W3: Data Mining and the Grid (half day) [* Starts at 0830]
W4: Temporal Data Mining: Algorithms, Theory and Applications (half day) [** Finishes 1800]
W5: Frequent Itemsets Mining Implementations (half day)
W6: Alternative Techniques for Data Mining and Knowledge Discovery (half day)
W7: Privacy and Security Aspects of Data Mining (half day) [** Finishes at 1800]
Tuesday November 2nd 2004

                                   Paganini Ballroom
                                    Opening Remarks
0930                                Paganini Ballroom
  -               Invited Talk: David Hand (Imperial College, London)
1030         Deception, Distortion and Discovery: Data Quality in Data Mining
  -                                Refreshment Break
       Paganini Ballroom           Regency Room                   Gresham Room
             SVM                 Quality Assessment 1              Data Streams
  -                                   Lunch Break
1400                                Paganini Ballroom
  -             Invited Talk: Thorsten Joachims (Cornell University, USA)
1500                       Learning to Predict Complex Objects
  -                                Refreshment Break
       Paganini Ballroom            Regency Room                Gresham Room
         Frequent Sets 1             Clustering 1              Web and Text Mining
                                  Paganini Ballroom
                                 Conference Reception

Wednesday November 3rd 2004

0900                                  Paganini Ballroom
  -     Invited Talk: Wray Buntine (Helsinki Institute of Information Technology, Finland)
1000                  Open-source Search Engines: A Data Mining Platform
  -                                   Refreshment Break
        Paganini Ballroom              Regency Room                    Gresham Room
             Bayes                   Quality Assessment 2                Time Data
  -            Excursion: Portsmouth Historic Dockyard (coaches leave 12.15)
1930                  Conference Dinner (Donatellos, 1-3 Brighton Place)

Thursday November 4th 2004

0900                                   Paganini Ballroom
  -                  Invited Talk: Ming Li (University of Waterloo, Canada)
1000                       Faster and More Sensitive Homology Search
  -                                   Refreshment Break
        Paganini Ballroom               Regency Room                   Gresham Room
          Structured Data                 Ensembles                     Pre-processing
                                    Lunch Break
     1315-1400. ICDM/TCCI Business Meeting. Paganini Ballroom. OPEN MEETING
                                      Paganini Ballroom
                           Panel Session: Data Mining - Where to Go?
  -                                   Refreshment Break
        Paganini Ballroom               Regency Room                   Gresham Room
       Feature Transformation            Clustering 2                  Frequent Sets 2

ICDM '04             Monday November 1, 2004
There are refreshment breaks from 10.30-11.00 and from 3.30-4.00 pm The lunch break
is from 12.30 to 2 pm (lunch is not included in the registration fee).

Morning (9 am – 12.30 pm)
    Gresham Room           10 Data Mining Mistakes - and How to Avoid Them
                           John F. Elder IV (Elder Research, Inc.)

    Fécamp Room            Data Mining In Time Series Databases
                           Eamonn Keogh (University of California, Riverside)

Afternoon (2 pm – 5.30 pm)
    Gresham Room           Algorithmic Excursions in Data Streams
                           Sudipto Guha (University of Pennsylvania)

    Fécamp Room            Data Grid Management Systems
                           Arun Swaran Jagatheesan (University of California at
                           San Diego)

All Day Workshops (9 am to 5 pm)

There are refreshment breaks from 10.30-11.00 and from 3.30-4.00 pm The lunch break
is from 12.00 to 1.30 pm (lunch is not included in the registration fee).

    Paganini Ballroom      Life Sciences Data Mining
                           Organizers: Chung-Sheng Li, IBM Thomas J. Watson
                           Research Center, Stephen Wong Harvard Medical School

    Regency Room           Foundations of Data Mining
                           Organizers: T.Y. Lin, San Jose State, USA, Stephen Smale,
                           Toyota Technological Institute at Chicago, Tomaso Poggio,

Morning Workshops (9 am to 12 noon)
There will be a refreshment break from 10.30-11.00.

    8.30 am to 12 noon
    Boardroom               Data Mining and the Grid
                            Organizers: Assaf Schuster, Ran Wolff, Technion, Israel

   9 am to 12 noon
   Syndicate Room 4              Frequent Itemsets Mining Implementations
                                Organizers: Roberto Bayardo, IBM Almaden Research
                                Center, Bart Goethals, Helsinki Institute for Information
                                Technology, Finland, Mohammed J. Zaki, Rensselaer
                                Polytechnic Institute

Afternoon Workshops
There will be a refreshment break from 3.30-4.00 pm

   1.30 pm to 6 pm
   Boardroom                    Temporal Data Mining: Algorithms, Theory and
                                Organizers: Sheng Ma, IBM T.J. Watson Research
                                Center, Tao Li, University of Rochester, Charles Perng,
                                IBM T.J. Watson Research Center

   1.30 pm to 5.30 pm
   Syndicate Room 4             Alternative Techniques for Data Mining and
                                Knowledge Discovery
                                Organizers: Juan Carlos Cubero, Daniel Sanchez,
                                University of Granada, Spain, Z. Ras, UNC Charlotte,
                                Thomas Sudkamp, Iona College

   1.30 pm to 6 pm
   Syndicate Room 6             Privacy and Security Aspects of Data Mining
                                Organizers: LiWu Chang, NRL; Carlisle Adams, Stan
                                Matwin, Justin Zhan, University of Ottawa

ICDM '04                 Tuesday November 2nd 2004
9.00-09.30 am             Opening Remarks (Paganini Ballroom)

9.30-10.30 am             Invited Talk (Paganini Ballroom)
        Deception, Distortion and Discovery: Data Quality in Data Mining
        David Hand (Imperial College, London)

        Data mining is typically a process of secondary data analysis, using data that were
        originally collected for some other purpose. They may have been of high quality for that
        purpose, but of low quality for the unspecified future analyses of data mining and it may
        be economically impracticable to require high quality data for all possible future
        analyses. This talk gives an overview of data quality, covering definitions,
        measurement, monitoring and improvement. Some important special topics are
        discussed in detail, including missing values, anomaly detection and deliberate data
        distortion. The talk is illustrated with real examples from a wide variety of areas.

10.30-11.00 am             Refreshment Break

11.00 am-12.30 pm                 Technical Sessions (3 Parallel Tracks)
         Regular papers are allocated 20 minutes for presentation and 2 minutes for questions.
         Short papers are allocated 10 minutes for presentation and 1 minute for questions.

Paganini Ballroom: SVM (3 Regular, 2 Short). Chair: Ning Zhong

Regular Papers (22 minutes)
A Bayesian Framework for Regularized SVM Parameter Estimation. Jens Gregor and
Zhenqiu Liu

Aligning Boundary in Kernel Space for Learning Imbalanced Dataset. Gang Wu and
Edward Chang

Non-Redundant Data Clustering. David Gondek and Thomas Hofmann

Short Papers (11 minutes)
Sparse Kernel Least Squares Classifier. Ping Sun

SVM and Graphical Algorithms: a Cooperative Approach. Francois Poulet

Regency Room: Quality Assesment 1 (3 Regular, 2 Short). Chair: Maggie Dunham

Regular Papers (22 minutes)
Transduction and typicalness for quality assessment of individual classifications in
machine learning and data mining. Matjaz Kukar

A Transaction-based Neighbourhood-driven Approach to Quantifying Interestingness of
Association Rules. Balasubramanian Shekar and Rajesh Natarajan

Cost-guided Class Noise Handling for Effective Cost-sensitive Learning. Xingquan Zhu
and Xindong Wu

Short Papers (11 minutes)
Estimation of false negatives to predict the accuracy of the classifier. Sandeep Mane,
Jaideep Srivastava and San-Yih Hwang

An Evaluation of Approaches to Classification Rule Selection. Frans Coenen and Paul

Gresham Room: Data Streams (2 Regular, 4 Short). Chair: Gagan Agrawal

Regular Papers (22 minutes)
Dynamic Classifier Selection for Effective Mining from Noisy Data Streams. Xingquan
Zhu and Xindong Wu

Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window. Yun Chi,
Haixun Wang, Philip Yu and Richard Muntz

Short Papers (11 minutes)
Decision Tree Evolution Using Limited Number of Labelled Items from Drifting Data
Streams. Wei Fan, Yi-an Huang and Philip Yu

AGILE: A General Approach to Detect Transitions in Evolving Data Streams. Jiong Yang
and Wei Wang

An Adaptive Learning Approach for Noisy Data Streams. Fang Chu, Yizhou Wang and
Carlo Zaniolo

Clustering on Demand for Multiple Data Streams. Bi-Ru Dai, Ming-Syan Chen, Jen-Wei
Huang and Mi-Yen Yeh

12.30-2.00 pm              Lunch (not included in registration fee)

2.00-3.00 pm               Invited Talk (Paganini Ballroom)
         Learning to Predict Complex Objects
         Thorsten Joachims, Cornell University, USA

         Over the last decade, much of the research on discriminative learning has focused on
         problems like classification and regression, where the prediction is a single univariate
         variable. But what if we need to predict complex objects like trees, sequences, or
         orderings? Such problems arise, for example, when a natural language parser needs to
         predict the correct parse tree for a given sentence, when a navigation assistant needs
         to predict the route a user prefers for getting to the destination, or when a search
         engine needs to predict which ranking is best for a given query. This talk will explore
         the challenges in predicting complex objects. In particular, I will discuss support vector
         approaches that cover some of these problems. They generalize the idea of margins to
         complex prediction problems and a large range of loss functions. While the resulting
         training problems have exponential size, there is a simple algorithm that allows training
         in polynomial time. Empirical results will be given for several examples.

3.00-3.30 pm               Refreshment Break

3.30-5.30 pm               Technical Sessions (3 Parallel Tracks)
Paganini Ballroom: Frequent Sets 1 (3 Regular, 5 Short). Chair: Osmar Zaiane

Regular Papers (22 minutes)
On Closed Constrained Frequent Pattern Mining. Francesco Bonchi and Claudio

Mining Frequent Itemsets from Secondary Memory. Gosta Grahne and Jianfei Zhu

Dependencies between transcription factor binding sites: comparison between ICA,
NMF, PLSA and frequent sets. Heli Hiisilä and Ella Bingham

Short Papers (11 minutes)
Incremental Mining of Frequent XML Query Patterns. Yi Chen and Yu Guo Wang

Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining.
Hwanjo Yu, Duane Searsmith, Xiaolei Li and Jiawei Han

Mining Frequent Closed Patterns in Microarray Data. Gao Cong, Kian-Lee Tan, Anthony
K. H. Tung and Feng Pan

Dryade: a new approach for discovering closed frequent trees in heterogeneous tree
databases. Alexandre Termier, Marie-Christine Rousset and Michele Sebag

Quantitative Association Rules Based on Half Spaces: An Optimization Approach. Ulrich
Rückert, Lothar Richter and Stefan Kramer

Regency Room: Clustering 1 (3 Regular, 5 Short). Chair: Christian Bohm

Regular Papers (22 minutes)
Fast and Exact Out-of-Core K-means Clustering. Anjan Goswami, Ruoming Jin and
Gagan Agrawal

SUMMARY: Efficiently Summarizing Transactions for Clustering. Jianyong Wang and
George Karypis

Subspace Selection for Clustering High-Dimensional Data. Christian Baumgartner, Karin
Kailing, Hans-Peter Kriegel, Peer Kröger and Claudia Plant

Short Papers (11 minutes)
Metric Incremental Clustering of Nominal Data. Dan Simovici, Namita Singla and
Michael Kuperberg

Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. Christoph
Eick, Nidal Zeidat and Ricardo Vilalta

Cluster Cores-based Clustering for High Dimensional Data. Yi-Dong Shen, Zhiyong
Shen, Shiming Zhang and Qiang Yang

Revealing True Subspace Clusters in High Dimensions. Jinze Liu, Karl Strohmaier and
Wei Wang

Feature-Based Prediction of Unknown Preferences for Nearest-Neighbor Collaborative
Filtering. Hyungil Kim, Juntae Kim and Jonathan Herlocker

Gresham Room: Web and Text Mining (2 Regular, 7 Short). Chair: Chris Clifton

Regular Papers (22 minutes)
Hybrid pre-query term expansion using Latent Semantic Analysis. Laurence Park and
Kotagiri Ramamohanarao

Improving Text Classification using Local Latent Semantic Indexing. Tao Liu, Zheng
Chen, Benyu Zhang, Wei-ying Ma and Gongyi Wu

Short Papers (11 minutes)
Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas.
Shalendra Chhabra, William Yerazunis and Christian Siefkes

SVD based Term Suggestion and Ranking System. David Gleich and Leonid Zhukov

Classifying Biomedical Citations without Labeled Training Examples. Xiaoli Li

Mining web data to create online navigation recommendations. Juan Velasquez,
Alejandro Bassi, Hiroshi Yasuda and Terumasa Aoki

Learning Conditional Independence Trees for Ranking. Harry Zhang and Jiang Su

On Ranking Refinements in the step-by-step Searching through a Product Catalogue.
Nenad Stojanovic

Supervised Latent Semantic Indexing for Document Categorization. Jian-Tao Sun,
Zheng Chen, Hua-Jun Zeng, Yu-Chang Lu, Chun-Yi Shi and Wei-Ying Ma

ICDM '04                  Wednesday November 3rd 2004
9.00-10.00 am              Invited Talk (Paganini Ballroom)
           Open-source Search Engines: A Data Mining Platform
           Wray Buntine, Helsinki Institute of Information Technology, Finland

           The ALVIS Consortium (http://www.alvis.info) and Search-Ina-Box are two projects
           that build open source search engines. The ALVIS partners believe the open source
           philosophy is ideal for next generation search platforms and are bringing data mining
           researchers together with search and information retrieval professionals to develop
           such a platform. This talk will outline the tasks and the relevance of the effort to the
           data mining community.

10.00-10.30 am               Refreshment Break

10.30 am-12.00 noon                  Technical Sessions (3 Parallel Tracks)
Paganini Ballroom: Bayes (2 Regular, 4 Short). Chair: Francois Poulet

Regular Papers (22 minutes)
Test-Cost Sensitive Naive Bayes Classification. Xiaoyong Chai, Lin Deng, Qiang Yang
and Charles X. Ling

Learning Concise and Accurate Naive Bayes Classifiers From Attribute Value
Taxonomies and Data. Jun Zhang and Vasant Honavar

Short Papers (11 minutes)
Learning Weighted Naive Bayes with Accurate Ranking. Shengli Sheng

Improving the Reliability of Decision Tree and Naive Bayes Learners. David Lindsay and
Sian Cox

Extensible Markov Model. Margaret Dunham, Yu Meng and Jie Huang

Privacy Sensitive Bayesian Network Parameter Learning. Da Meng, Krishnamoorthy
Sivakumar and Hillol Kargupta

Regency Room: Quality Assesment 2 (2 Regular, 2 Short). Chair: Wei Wang

Regular Papers (22 minutes)
Communication Efficient Construction of Decision Trees Over Heterogeneously
Distributed Data. Todd Olsen, Chris Giannella, Kun Liu and Hillol Kargupta

SCHISM: A New Approach for Interesting Subspace Mining. Karlton Sequeira and
Mohammed Zaki

Short Papers (11 minutes)
Divide and Prosper: Comparing Models of Customer Behavior. Tianyi Jiang and
Alexander Tuzhilin

Relational Peculiarity Oriented Data Mining. Ning Zhong, Chunnian Liu, Y.Y. Yao,
Muneaki Ohshima, Mingxin Huang, Jiajin Huang

Gresham Room: Time Data (2 Regular, 4 Short). Chair: Srinivasan Parthasarthy

Regular Papers (22 minutes)
Unimodal segmentation of sequences. Niina Haiminen and Aristides Gionis

Detection of Significant Sets of Episodes in Event Sequences. Mikhail Atallah, Robert
Gwadera and Wojciech Szpankowski

Short Papers (11 minutes)
Dynamic Daily-living Patterns and Association Analyses in Tele-care Systems. Beum-
Seuk Lee, Trevor Martin, Nick Clarke, Basim Majeed and Detlef Nauck

Mining Temporal Patterns Without Predefined Time Windows. Tao Li and Sheng Ma

Finding Constrained Frequent Episodes Using Minimal Occurrences. Xi Ma, HweeHwa
Pang and Kian-Lee Tan

Predicting Density-Based Spatial Clusters Over Time. Chih Lai and Nga Nguyen

12.00-6.30 pm Excursion to Portsmouth Historic Dockyard
     Coaches leave from outside Old Ship Hotel at 12.15 pm
                     (Ticket holders only)

7.30 pm         Conference Dinner at Donatellos, 1-3 Brighton Place

ICDM '04                  Thursday November 4th 2004
9.00-10.00 am             Invited Talk (Paganini Ballroom)
         Faster and More Sensitive Homology Search
         Ming Li, University of Waterloo, Canada

         Homology search is the most popular task in bioinformatics. It is probably one of the
         largest data mining tasks second only to internet search. In the late 1970s, dynamic
         programming for homology search was introduced. In the 1980s Blast heuristics was
         introduced to trade sensitivity with speed. Today, a large fraction of the world's
         supercomputing time is consumed by Blast and Smith-Waterman dynamic
         programming. The explosive growth of genomics data demands significantly more
         sensitive (than Blast) and faster (than both Smith-Waterman and Blast) homology
         search software. Can we speed up homology search without compromising sensitivity?
         We introduce the fundamental ideas and the mathematical theory of optimized spaced
         seeds. Equipped with optimal spaced seeds, our program PatternHunter runs many
         times faster than Blast, at higher sensitivity levels. With multiple optimized spaced
         seeds, PatternHunter runs 3000 times faster than Smith-Waterman, at the same (full)
         sensitivity, bringing homology search back full circle.

10.00-10.30 am              Refreshment Break

10.30 am-12.30 pm                 Technical Sessions (3 Parallel Tracks)
Paganini Ballroom: Structured Data (2 Regular, 6 Short). Chair: Aristides Gionis

Regular Papers (22 minutes)
Efficient Density-Based Clustering of Complex Objects. Stefan Brecheisen, Hans-Peter
Kriegel and Martin Pfeifle

Dependency Networks for Relational Data. Jennifer Neville and David Jensen

Short Papers (11 minutes)
GREW---A Scalable Frequent Subgraph Discovery Algorithm. Michihiro Kuramochi and
George Karypis

Discovery of Functional Relationships in Multi-relational Data using Inductive Logic
Programming. Alexessander Alves, Rui Camacho and Eugenio Oliveira

Mining Generalized Substructures from a Set of Labeled Graphs. Akihiro Inokuchi

Orthogonal Decision Trees. Hillol Kargupta and Haimonti Dutta

Scalable Multi-Relational Association Mining. Amanda Clare, Hugh Williams and
Nicholas Lester

Efficient Relationship Pattern Mining using Multi-relational Iceberg-Cubes. Dawit Seid
and Sharad Mehrotra

Regency Room: Ensembles (3 Regular, 5 Short). Chair: Hillol Kargupta

Regular Papers (22 minutes)
Semi-Supervised Mixture-of-Experts Classification. Grigoris Karakoulas and Ruslan

IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects.
Gui-Rong Xue, Dou Shen, Qiang Yang, Hua-Jun Zeng, Zheng Chen and Wei-Ying Ma

A Polygonal Line Algorithm based Nonlinear. Feng Zhang and Danieal Apley

Short Papers (11 minutes)
A Greedy Algorithm for Selecting Models in Ensembles. Andrei Turinsky and Robert

Text Classification by Boosting Weak Learners based on Terms and Concepts. Stephan
Bloehdorn and Andreas Hotho

Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization. Chenyong
Hu, Benyu Zhang, Shuicheng Yan, Qiang Yang, Zheng Chen and Weiying Ma

A Machine Learning Approach To Improve Congestion Control Over Wireless Computer
Networks. Pierre Geurts, Ibtissam El Khayat and Guy Leduc

Detecting Patterns of Appliances from Total Load Data Using a Dynamic Programming
Approach. Michael Baranski and Jürgen Voss

Gresham Room: Pre-processing (3 Regular, 5 Short). Chair: Tsau Lin

Regular Papers (22 minutes)
On Local Spatial Outliers. Pei Sun and Sanjay Chawla

Privacy-Preserving Outlier Detection. Jaideep Vaidya and Chris Clifton

Bottom-Up Generalization: A Data Mining Solution To Privacy Protection. Ke Wang,
Philip Yu and Sourav Chakraborty

Short Papers (11 minutes)
RDF: A Density-based Outlier Detection using Vertical Data Representation. Dongmei

LOADED: Link-based Outlier and Anomaly Detection in Evolving Data Sets. Amol
Ghoting, Matthew Otey and Srinivasan Parthasarathy

MMSS: Multi-modal Story-oriented Video Summarization. Jia-Yu Pan, Hyungjeong Yang
and Christos Faloutsos

The Anatomy of a Hierarchical Clustering Engine for Web-page Snippets. Antonio Gulli
and Paolo Ferragina

Using Emerging Patterns and Decision Trees in Rare-class Classification. Hamad
Alhamady and Kotagiri Rao

12.30-2.00 pm           Lunch (not included in registration fee)

1.15-2.00 pm        ICDM/TCCI Open Meeting (Paganini Ballroom)

2.00-3.00 pm            Panel Discussion (Paganini Ballroom)
           Data Mining - Where to Go? Organizer: Arno Siebes

           Question 1: Is there a future in industrial applications?
           Question 2: Will Data Mining go down under its own success?
           Question 3: Are there any interesting problems left?

3.00-3.30 pm            Refreshment Break

3.30-5.30 pm            Technical Sessions (3 Parallel Tracks)
Paganini Ballroom: Feature Selection (2 Regular, 7 Short). Chair: Bill Perrizzo

Regular Papers (22 minutes)
Generation of Attribute Value Taxonomies from Data and Their Use in Data-Driven
Construction of Accurate and Compact Classifiers. Dae-Ki Kang, Adrian Silvescu, Jun
Zhang and Vasant Honavar

A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering
New Attributes. Tak-Lam Wong and Wai Lam

Short Papers (11 minutes)
Attribute Measurement Policies for Cost-effective Classification. Andrew Arnt and
Shlomo Zilberstein

Feature Selection via Supervised Model Construction. Yue Huang, Paul McCullagh,
Norman Black and Roy Harper

Filling-in Missing Objects in Orders. Toshihiro Kamishima and Shotaro Akaho

A Biobjective Model To Select Features With Good Classification Quality And Low Cost.
Emilio Carrizosa, Belen Martin-Barragan and Dolores Romero Morales

Correlation Preserving Discretization. Sameep Mehta, Srinivasan Parthasarathy and Hui

A Comparative Study of Linear and Nonlinear Feature Extraction Methods. Cheong Hee
Park and Haesun Park

Active Feature-Value Acquisition for Classifier Induction. Prem Melville, Maytal Saar-
Tsechansky, Foster Provost and Raymond Mooney

Regency Room: Clustering 2 (4 Regular, 3 Short). Chair: Shusaku Tsumoto

Regular Papers (22 minutes)
Density Connected Clustering with Local Subspace Preferences. Christian Böhm, Karin
Kailing, Hans-Peter Kriegel and Peer Kröger

Mass Spectrum Labeling: Theory and Practice. Zheng Huang, Lei Chen, Jin-Yi Cai,
Deborah Gross, David Musicant, Raghu Ramakrishnan and James J. Schauer

Multi-View Clustering. Steffen Bickel and Tobias Scheffer

Analysis of Consensus Partition in Cluster Ensemble. Alexander Topchy, Martin Law,
Anil Jain and Ana Fred

Short Papers (11 minutes)
Evolutionary Algorithms for Clustering Gene-Expression Data. Eduardo Hruschka,
Leandro Castro and Ricardo Campello

Evaluating Attraction in Spatial Point Patterns with an Application in the Field of Cultural
History. Marko Salmenkivi

An Adaptive Density-Based Clustering Algorithm for Spatial Database with Noise.
Daoying Ma and Aidong Zhang

Gresham Room: Frequent Sets (3 Regular, 5 Short). Chair: Bart Goethals

Regular Papers (22 minutes)
Mining Associations by Solving Integral Linear Inequalities. Tsau Lin

Probabilistic Principal Surfaces for Yeast Gene Microarray Data Mining. Antonino
Staiano, Roberto Tagliaferri, Giancarlo Raiconi, Giuseppe Longo, Gennaro Miele and
Diego Di Bernardo

MMAC: A New Multi-class, Multi-label Associative Classification Approach. Fadi
Thabtah, Peter Cowling and Yonghong Peng

Short Papers (11 minutes)
Alpha Galois Lattices. Véronique Ventos, Henry Soldano and Thibaut Lamadon

Query-Driven Support Pattern Discovery for Classification Learning. Yiqiu Han and Wai

Learning Rules from Highly Unbalanced Data Sets. Jianping Zhang, Eric Bloedorn,
Lowell Rosen and Daniel Venese

Matching in Frequent Tree Discovery. Björn Bringmann

Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association
Rules Mining. Mehmet Kaya and Reda Alhajj


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