Proceedings of the 2001 SIAM International Conference on Data Mining
Each link below is to a PDF of the paper as it was submitted. Papers are listed in program order. PDF file names represent the Proceedings (DM and year 01), followed by order in printed version (e.g. 001) and first author's last name and first initial.
First SIAM International Conference on
Data Mining
Vipin
Kumar and Robert Grossman, eds.
Derivative Dynamic Time Warping
Eamonn
J. Keogh and Michael J. Pazzani
Decomposition of Event Sequences into Independent Components
Heikki
Mannila and Dmitry Rusakov
Finding Similar Situations in Sequences of Events Via Random Projections
Heikki
Mannila and Jouni K. Seppänen
Selective Markov Models for Predicting Web-Page Accesses
Mukund
Deshpande and George Karypis
On the Performance of Bisecting K-Means and PDDP
Sergio
M. Savaresi and Daniel L. Boley
Generalized K-Harmonic Means -- Dynamic Weighting of Data in Unsupervised
Learning
Bin
Zhang
Adaptive Grids for Clustering Massive Data Sets
Harsha
Nagesh, Sanjay Goil, and Alok Choudhary
Detecting Seasonal Trends and Cluster Motion Visualization for Very High Dimensional Transactional
Data
Gunjan
K. Gupta and Joydeep Ghosh
Cheaper by the Dozen: Batched Algorithms
Ben
Gum and Richard Lipton
Incremental Mining of Constrained Association Rules
Ahmed
M. Ayad, Nagwa M. El-Makky, and Yousry Taha
Using Simulated Pseudo Data to Speed Up Statistical Predictive Modeling from
Massive Data Sets
Ramesh
Natarajan and Edwin Pednault, IBM, Thomas J. Watson Research Center
A Middleware for Developing Parallel Data Mining Applications
Ruoming
Jin and Gagan Agrawal
Shared State for Client-Server Mining
Srinivasan
Parthasarathy and Sandhya Dwarkadas
A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed
Environment
Byung-Hoon
Park, Rajeev Ayyagari and Hillol Kargupta
Automatic Textual Document Categorization Using Multiple Similarity-Based
Models
Kwok-Yin
Lai and Wai Lam
Dimension Reduction Based on Centroids and Least Squares for Efficient Processing
of Text Data
M.
Jeon, H. Park, and J. B. Rosen
Hierarchical Classification of Real Life Documents
Ke
Wang, Senqiang Zhou, and Yu He
Improved Spatial-Temporal Forecasting through Modeling of Spatial Residuals
in Recent History
Dragoljub
Pokrajac and Zoran Obradovic
Methods for Large-Scale Mining of Networks of Human Genes
Tor-Kristian
Jenssen, Lisa M.J. Öberg, Magnus L. Anderson, and Jan Komorowski
Mining the Detector Responses of a Conducting Polymer Composite-Based Electronic
Nose
M. C. Burl, S. Briglin, B. Doleman, A. Hopkins, A. Matzger, D. N. Ortiz, A.
Schaffer, S. Upchurch, T. Vaid, and
N. S. Lewis
Looking for Nonlinearities in the Large Scale Dynamics of the Atmosphere
Claudia
Tebaldi, Doug Nychka, and Grant Branstator
RSVM: Reduced Support Vector Machines
Yuh-Jye
Lee and Olvi L. Mangasarian
Boosting Localized Classifiers in Heterogeneous Databases
Aleksandar
Lazarevic and Zoran Obradovic
Generating Classification Rules According to User's Existing Knowledge
Shu
Chen and Bing Liu
Information-Theoretic Feature Crediting in Multiclass Support Vector Machines
Vikas
Sindhwani, Pushpak Bhattacharya, and Subrata Rakshit
Mining Preferences from Spatial-Temporal Data
Donald
E. Brown, Hua Liu and Yifie Xue
Modeling Spatial Dependencies for Mining Geospatial Data
Sanjay
Chawla, Shashi Shekhar, Weili Wu, and Uygar Ozesmi
Detecting Novel Network Intrusions Using Bayes Estimators
Daniel
Barbará, Ningning Wu, and Sushil Jajodia
PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case
Study in
Network
Intrusion Detection)
Ramesh
C. Agarwal and Mahesh V. Joshi
