Elements of information theory
Elements of information theory
Classification of binary vectors by stochastic complexity
Journal of Multivariate Analysis
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient multi-way text categorization via generalized discriminant analysis
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A general model for clustering binary data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Unified View on Clustering Binary Data
Machine Learning
Efficiently clustering transactional data with weighted coverage density
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic segmentation with shared topic detection and alignment of multiple documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
k-ANMI: A mutual information based clustering algorithm for categorical data
Information Fusion
Determining the best K for clustering transactional datasets: A coverage density-based approach
Data & Knowledge Engineering
Efficient layered density-based clustering of categorical data
Journal of Biomedical Informatics
Extending the rand, adjusted rand and jaccard indices to fuzzy partitions
Journal of Intelligent Information Systems
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
Context-Based Distance Learning for Categorical Data Clustering
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
SCALE: a scalable framework for efficiently clustering transactional data
Data Mining and Knowledge Discovery
Hierarchical density-based clustering of categorical data and a simplification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Online entropy-based model of lexical category acquisition
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Parameter-free anomaly detection for categorical data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
A graph model for mutual information based clustering
Journal of Intelligent Information Systems
A diversity measure leveraging domain specific auxiliary information
Proceedings of the 20th ACM international conference on Information and knowledge management
CPCQ: Contrast pattern based clustering quality index for categorical data
Pattern Recognition
DHCC: Divisive hierarchical clustering of categorical data
Data Mining and Knowledge Discovery
From Context to Distance: Learning Dissimilarity for Categorical Data Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Detection of HTTP-GET attack with clustering and information theoretic measurements
FPS'12 Proceedings of the 5th international conference on Foundations and Practice of Security
Central clustering of categorical data with automated feature weighting
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
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Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity co-efficients. An iterative Monte-Carlo procedure is then presented to search for the partitions minimizing the criterion. Experiments are conducted to show the effectiveness of the proposed procedure.