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Home > Proceedings of the Fourth SIAM International Conference on Data Mining / > Active semi-supervision for pairwise constrained clustering
Proceedings of the Fourth SIAM International Conference on Data Mining / |
Berry, Michael W.
Society for Industrial and Applied Mathematics,
Proceedings of the Fourth SIAM International Conference on Data Mining /
Active semi-supervision for pairwise constrained clustering
Authors

Sugato Basu, Arindam Banerjee and Raymond J. Mooney

ARI Id

1675746019892_53425699

Access

Open/Free Access

Table of Contents of Book
Showing 1 to 20 of 61 entries
Chapters/HeadingsAuthor(s)PagesInfo
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 Kroger, Alexej Pryakhin and Matthias Schubert
Minimum sum-squared residue co-clustering of gene expression data
Hyuk Cho, Inderjit S. Dhillon, Yuqlang 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 Koyuturk 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
Alipio 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
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 61 entries
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