Algorithms for clustering data
Algorithms for clustering data
Elements of information theory
Elements of information theory
Applied multivariate techniques
Applied multivariate techniques
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
An entropic estimator for structure discovery
Proceedings of the 1998 conference on Advances in neural information processing systems II
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Cluster validity methods: part I
ACM SIGMOD Record
Applications of Data Mining in Computer Security
Applications of Data Mining in Computer Security
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Finding Localized Associations in Market Basket Data
IEEE Transactions on Knowledge and Data Engineering
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The learning-curve sampling method applied to model-based clustering
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
VISTA: validating and refining clusters via visualization
Information Visualization
On efficiently summarizing categorical databases
Knowledge and Information Systems
The "Best K" for entropy-based categorical data clustering
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Finding centric local outliers in categorical/numerical spaces
Knowledge and Information Systems
Knowledge and Information Systems
MAR: Maximum Attribute Relative of soft set for clustering attribute selection
Knowledge-Based Systems
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The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been developed, surprisingly, none has satisfactorily addressed the problem of best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure, traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper, we study the entropy property between the clustering results of categorical data with different K number of clusters, and propose the BKPlot method to address the three important cluster validation problems: (1) How can we determine whether there is significant clustering structure in a categorical dataset? (2) If there is significant clustering structure, what is the set of candidate “best Ks”? (3) If the dataset is large, how can we efficiently and reliably determine the best Ks?