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Part I: Visualization and Applications
Visualizing Clustering Results
Ian Davidson
VizCluster: An Interactive Visualization
Approach to Cluster Analysis and Its Application on Microarray Data
Li Zhang, Chung Tang, Yong Shi, Yuqing Song, Aidong Zhang, and Murali Ramanathan
Ensemble-based Adaptive Intrusion Detection
Wei Fan and Salvatore J. Stolfo
Instance Selection Techniques for Memory-based
Collaborative
Filtering Kai Yu, Xiaowei Xu, Jianjua Tao, Martin Ester, and Hans-Peter Kriegel
Part II: Mining Large Data Sets
Shared Memory Paraellization of Data
Mining Algorithms: Techniques, Programming Interface, and Performance
Ruoming Jin and Gagan Agrawal
A Data Parallel Approach for Large-Scale
Gaussian Process Modeling
Arindam Choudhury, Prasanth B. Nair, and Andy J. Keane
Efficient Filtering of Large Dataset—A
User-Centric Paradigm
Yi Xia, Wei Wang, Jiong Yang, Philip Yu, and Richard Muntz
Why the Information Explosion Can Be
Bad for Data Mining, and How Data Fusion Provides a Way Out
Peter van der Putten, Joost N. Kok, and Amar Gupta
Part III: Mining Sequential and Structured Patterns
On the Optimal Clustering of Sequential
Data
Cheng-Run Lin and Ming-Syan Chen
Efficient Substructure Discovery from
Large Semi-structured Data
Tatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura, Hiroshi Satamoto,
and Setsuo Arikawa
Discovering Frequent Substructures from
Hierarchical Semi-structured Data
Gao Cong, Lan Yi, Bing Liu, and Ke Wang
Part IV: Time Series Analysis
Iterative Deepening Dynamic Time Warping
for Time Series
Selina Chu, Eammon Keogh, David Hart, and Michael Pazzani
Extracting Precursor Rules from Time
Series—A Classical Statistical Viewpoint
Joăo B. D. Cabrera and Raman K. Mehra
Autoregressive Tree Models for Time-Series
Analysis
C. Meek, D. M. Chickering, and D. Heckerman
Part V: Support Vector Machine and Neural Networks
Incremental Support Vector Machine Classification
Glenn Fung and Olvi Mangasarian
A Pattern Search Method for Model Selection
of Support Vector Regression
Michinari Momma and Kristin P. Bennett
Explicit Thermodynamic Properties using
Radial Basis Functions Neural Networks
Olivier Adam and Olivier Léonard
Part VI: Clustering
Cluster Selection in Divisive Clustering
Algorithms
Sergio M. Savaresi, Daniel L. Boley, Sergio Bittanti, and Giovanna Gazzaniga
A Clustering Technique for Mining Data
from Text Tables
Hasan Davulcu, Saikat Mukherjee, and I. V. Ramakrishnan
On Scaling Up Balanced Clustering Algorithms
Arindam Banerjee and Joydeep Ghosh
Part VII: Classification and Decision Tables
Efficient Local Flexible Nearest Neighbor
Classification
Carlotta Domeniconi and Dimitrios Gunopulos
Approximate Splitting for Ensembles of
Trees using Histograms
Chandrika Kamath, Erick Cantú-Paz, and David Littau
The Power of Second-Order Decision Tables
R. Hewett and J. Leuchner
Part VIII: Causality Rules and Relation Learning
Mining Relationship between Triggering
and Consequential Events in a Short Transaction Database
Chang-Hung Lee, Philip S. Yu, and Ming-Syan Chen
Learning Simple Relations: Theory and
Applications
Pavel Berkhin and Jonathan D. Becher
A Framework for Scalable Cost-sensitive
Learning Based on Combing Probabilities and Benefits
Wei Fan, Haixun Wang, Philip Yu, and Sal Stolfo
Part IX: Mining Frequent Patterns
CHARM: An Efficient Algorithm for Closed
Itemset Mining
Mohammed J. Zaki and Ching-Jiu Hsiao
Evaluating the Performance of Association
Mining Methods in 3-D Medical Image Databases
Vasileios Megalooikonomou
Mining Frequent Itemsets in Evolving
Databases
A.A. Veloso, W. Meira, Jr., M. B. de Carvalho, B. Pôssas, S. Parthasarathy,
and M. Javeed Zaki
Discovering Fully Dependent Patterns
Feng Liang, Sheng Ma, and Joseph L. Hellerstein
Part X: Applications
One Step Evolutionary Mining of Context
Sensitive Associations and Web Navigation Patterns
O. Nasraoui and R. Krishnapuram
MedMeSH Summarizer: Text Mining for Gene
Clusters
P. Kankar, S. Adak, A. Sarkar, K. Murali, and G. Sharma
Segmented Regression Estimators for Massive
Data Sets
Ramesh Natarajan and Edwin Pednault
Collusion in the U. S. Crop Insurance
Program: Applied Data Mining
Bertis B. Little, Walter L. Johnston, Jr., Ashley C. Lovell, Roderkick M.
Rejesus, and Steve A. Steed