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Proceedings of the Fourth SIAM International Conference on Data Mining

Michael W. Berry, Umeshwar Dayal, Chandrika Kamath and David Skillicorn, Editors

Proceedings in Applied Mathematics 117

Conference held April 2004, Lake Buena Vista, Florida

The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who will discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques.

This proceedings includes 61 research papers; 23 were accepted as poster presentations, 26 were accepted as regular papers, and 12 were accepted as student papers from the conference.


Message from the Program Co-Chairs


Mining Relationships between Interacting Episodes
Carl Mooney and John F. Roddick

Making Time-Series Classification More Accurate Using Learned Constraints
Chotirat Ann Ratanamahatana and Eamonn Keogh

GRM: A New Model for Clustering Linear Sequences
Hansheng Lei and Venu Govindaraju

Nonlinear Manifold Learning for Data Stream
Martin H. C. Law, Nan Zhang, and Anil K. Jain

Text Mining from Site Invariant and Dependent Features for Information Extraction Knowledge Adaptation
Tak-Lam Wong and Wai Lam

Constructing Time Decompositions for Analyzing Time Stamped Documents
Parvathi Chundi and Daniel J. Rosenkrantz

Equivalence of Several Two-Stage Methods for Linear Discriminant Analysis
Peg Howland and Haesun Park

A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects
Hui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, and Jin Soung Yoo

A Top-Down Method for Mining Most Specific Frequent Patterns in Biological Sequences
Martin Ester and Xiang Zhang

Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
Hans-Peter Kriegel, Peer Kröger, Alexej Pryakhin, and Matthias Schubert

Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data
Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, and Suvrit Sra

Training Support Vector Machine Using Adaptive Clustering
Daniel Boley and Dongwei Cao

IREP++, A Faster Rule Learning Algorithm
Oliver Dain, Robert K. Cunningham, and Stephen Boyer

GenIc: A Single Pass Generalized Incremental Algorithm for Clustering
Chetan Gupta and Robert Grossman

CONQUEST: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attributed Datasets
Jie Chi, Mehmet Koyutürk, and Ananth Grama

Basic Association Rules
Guichong Li and Howard J. Hamilton

Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules
Alípio Jorge

Quantitative Evaluation of Clustering Results Using Computational Negative Controls
Ronald K. Pearson, Tom Zylkin, James S. Schwaber, and Gregory E. Gonye

An Abstract Weighting Framework for Clustering Algorithms
Richard Nock and Frank Nielsen

RBA: An Integrated Framework for Regression Based on Association Rules
Aysel Ozgur, Pang-Ning Tan, and Vipin Kumar

Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Wenliang Du, Yunghsiang S. Han, and Shigang Chen

Clustering with Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit Dhillon, and Joydeep Ghosh

Density-Connected Subspace Clustering for High-Dimensional Data
Karin Kailing, Hans-Peter Kriegel, and Peer Kröger

Tessellation and Clustering by Mixture Models and Their Parallel Implementations
Qiang Du and Xiaoqiang Wang

Clustering Categorical Data Using the Correlated-Force Ensemble
Kun-Ta Chuang and Ming-Syan Chen

HICAP: Hierarchical Clustering with Pattern Preservation
Hui Xiong, Michael Steinbach, Pang-Ning Tan, and Vipin Kumar

Enhancing Communities of Interest Using Bayesian Stochastic Blockmodels
Deepak Agrawal and Daryl Pregibon

VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring
Hillol Kargupta, Ruchita Bhargava, Kun Liu, Michael Powers, Patrick Blair, Samuel Bushra, James Dull, Kakali Sarkar, Martin Klein, Mitesh Vasa, and David Handy

DOMISA: DOM-Based Information Space Adsorption for Web Information Hierarchy Mining
Hung-Yu Kao, Jan-Ming Ho, and Ming-Syan Chen

CREDOS: Classification Using Ripple Down Structure (A Case for Rare Classes)
Mahesh V. Joshi and Vipin Kumar

Active Semi-Supervision for Pairwise Constrained Clustering
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney

Finding Frequent Patterns in a Large Sparse Graph
Michihiro Kuramochi and George Karypis

A General Probabilistic Framework for Mining Labeled Ordered Trees
Nobuhisa Ueda, Kiyoko F. Aoki, and Hiroshi Mamitsuka

Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
Ashok N. Srivastava

A Mixture Model for Clustering Ensembles
Alexander Topchy, Anil K. Jain, and William Punch

Visualizing RFM Segmentation
Ron Kohavi and Rajesh Parekh

Visually Mining through Cluster Hierarchies
Stefan Brechiesen, Hans-Peter Kriegel, Peer Kröger, and Martin Pfeifle

Class-Specific Ensembles for Active Learning in Digital Imagery
Amit Mandvikar and Huan Liu

Mining Text for Word Senses Using Independent Component Analysis
Reinhard Rapp

A Kernel-Based Semi-Naive Bayesian Classifier Using P-Trees
Anne Denton and William Perrizo

BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint
Jianyong Wang and George Karypis

A General Framework for Adaptive Anomaly Detection with Evolving Connectionist Systems
Yihua Liao, V. Rao Vemuri, and Alejandro Pasos

R-MAT: A Recursive Model for Graph Mining
Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos

Lazy Learning by Scanning Memory Image Lattice
Yiqiu Han and Wai Lam

Text Mining Using Non-negative Matrix Factorizations
V. Paul Pauca, Farial Shahnaz, Michael W. Berry, and Robert J. Plemmons

Active Mining of Data Streams
Wei Fan, Yi-an Huang, Haixun Wang, and Philip S. Yu

Learning to Read Between the Lines: The Aspect Bernoulli Model
A. Kabán, E. Bingham, and T. Hirsimäki

Exploiting Hierarchical Domain Values in Classification Learning
Yiqiu Han and Wai Lam

IFD: Iterative Feature and Data Clustering
Tao Li and Sheng Ma

Adaptive Filtering for Efficient Record Linkage
Lifang Gu and Rohan Baxter

A Foundational Approach to Mining Itemset Utilities from Databases
Hong Yao, Howard J. Hamilton, and Cory J. Butz

The Discovery of Generalized Causal Models with Mixed Variables Using MML Criterion
Gang Li and Honghua Dai

Reservoir-Based Random Sampling with Replacement from Data Stream
Byung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, and Al Geist

Principal Component Analysis and Effective K-Means Clustering
Chris Ding and Xiaofeng He

Classifying Documents without Labels
Daniel Barbará, Carlotta Domeniconi, and Ning Kang

Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model
Hyunsoo Kim and Haesun Park

Continuous-Time Bayesian Modeling of Clinical Data
Sathyakama Sandilya and R. Bharat Rao

Subspace Clustering of High Dimensional Data
Carlotta Domeniconi, Dimitris Papadopoulos, Dimitrios Gunopulos, and Sheng Ma

Privacy Preserving Naďve Bayes Classifier for Vertically Partitioned Data
Jaideep Vaidya and Chris Clifton

Resource-Aware Mining with Variable Granularities in Data Streams
Wei-Guang Teng, Ming-Syan Chen, and Philip S. Yu

Mining Patters of Activity from Video Data
Michael C. Burl

Author Index

2004 / xiv + 537 / Softcover / ISBN-13: 978-0-898715-68-2 / ISBN-10: 0-89871-568-7 /
List Price $161.00 / SIAM Member Price $112.70 / Order Code PR117
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Banner art adapted from a figure by Hinke M. Osinga and Bernd Krauskopf (University of Auckland, NZ.)