Other versions by this Author
| Mining relationships between interacting episodes | p. 1 |
| Making time-series classification more accurate using learned constraints | p. 11 |
| GRM : a new model for clustering linear sequences | p. 23 |
| Nonlinear manifold learning for data stream | p. 33 |
| Text mining from site invariant and dependent features for information extraction knowledge adaptation | p. 45 |
| Constructing time decompositions for analyzing time stamped documents | p. 57 |
| Equivalence of several two-stage methods for linear discriminant analysis | p. 69 |
| A framework for discovering co-location patterns in data sets with extended spatial objects | p. 78 |
| A top-down method for mining most specific frequent patterns in biological sequences | p. 90 |
| Using support vector machines for classifying large sets of multi-represented objects | p. 102 |
| Minimum sum-squared residue co-clustering of gene expression data | p. 114 |
| Training support vector machine using adaptive clustering | p. 126 |
| IREP++, a faster rule learning algorithm | p. 138 |
| Genic : a single pass generalized incremental algorithm for clustering | p. 147 |
| Conquest : a distributed tool for constructing summaries of high-dimensional discrete attributed datasets | p. 154 |
| Basic association rules | p. 166 |
| Hierarchical clustering for thematic browsing and summarization of large sets of association rules | p. 178 |
| Quantitative evaluation of clustering results using computational negative controls | p. 188 |
| An abstract weighting framework for clustering algorithms | p. 200 |
| RBA : an integrated framework for regression based on association rules | p. 210 |
| Privacy-preserving multivariate statistical analysis : linear regression and classification | p. 222 |
| Clustering with Bregman divergences | p. 234 |
| Density-connected subspace clustering for high-dimensional data | p. 246 |
| Tessellation and clustering by mixture models and their parallel implementations | p. 257 |
| Clustering categorical data using the correlated-force ensemble | p. 269 |
| HICAP : hierarchical clustering with pattern preservation | p. 279 |
| Enhancing communities of interest using Bayesian Stochastic Blockmodels | p. 291 |
| VEDAS : a mobile and distributed data stream mining system for real-time vehicle monitoring | p. 300 |
| DOMISA : DOM-based information space adsorption for web information hierarchy mining | p. 312 |
| CREDOS : classification using ripple down structure (a case for rare classes) | p. 321 |
| Active semi-supervision for pairwise constrained clustering | p. 333 |
| Finding frequent patterns in a large sparse graph | p. 345 |
| A general probabilistic framework for mining labeled ordered trees | p. 357 |
| Mixture density Mercer kernels : a method to learn kernels directly from data | p. 369 |
| A mixture model for clustering ensembles | p. 379 |
| Visualizing RFM segmentation | p. 391 |
| Visually mining through cluster hierarchies | p. 400 |
| Class-specific ensembles for active learning in digital imagery | p. 412 |
| Mining text for word senses using independent component analysis | p. 422 |
| A kernel-based semi-naive Bayesian classifier using P-trees | p. 427 |
| BAMBOO : accelerating closed itemset mining by deeply pushing the length-decreasing support constraint | p. 432 |
| A general framework for adaptive anomaly detection with evolving connectionist systems | p. 437 |
| R-MAT : a recursive model for graph mining | p. 442 |
| Lazy learning by scanning memory image lattice | p. 447 |
| Text mining using non-negative matrix factorizations | p. 452 |
| Active mining of data streams | p. 457 |
| Learning to read between the lines : the aspect Bernoulli model | p. 462 |
| Exploiting hierarchical domain values in classification learning | p. 467 |
| IFD : iterative feature and data clustering | p. 472 |
| Adaptive filtering for efficient record linkage | p. 477 |
| A foundational approach to mining itemset utilities from databases | p. 482 |
| The discovery of generalized causal models with mixed variables using MML criterion | p. 487 |
| Reservoir-based random sampling with replacement from data stream | p. 492 |
| Principal component analysis and effective K-means clustering | p. 497 |
| Classifying documents without labels | p. 502 |
| Data reduction in support vector machines by a kernelized ionic interaction model | p. 507 |
| Continuous-time Bayesian modeling of clinical data | p. 512 |
| Subspace clustering of high dimensional data | p. 517 |
| Privacy preserving naive Bayes classifer for vertically partitioned data | p. 522 |
| Resource-aware mining with variable granularities in data streams | p. 527 |
| Mining patters of activity from video data | p. 532 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
Proceedings Of The Fourth Siam International Conference On Data Mining
by Berry, Michael W.; Dayal, Umeshwar; Kamath, Chandrika; Skillicorn, DavidFormat: Paperback
Pub. Date: 2004-04-30
Publisher(s): Society for Industrial & Applied
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