Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Model-based Clustering with Soft Balancing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
Learning a subspace for clustering via pattern shrinking
Information Processing and Management: an International Journal
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We discuss extensions of the technique to the tasks of semi-supervised classification and enumeration of successive non-redundant clusterings. We present experimental results for applications in text mining and computer vision.