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Full-Text Articles in Physical Sciences and Mathematics
An Iterative Initial-Points Refinement Algorithm For Categorical Data Clustering, Ying Sun, Qiuming Zhu, Zhengxin Chen
An Iterative Initial-Points Refinement Algorithm For Categorical Data Clustering, Ying Sun, Qiuming Zhu, Zhengxin Chen
Computer Science Faculty Publications
The original k-means clustering algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. The k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference. World Scientific, Singapore, 1997, pp. 21–34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is …
Pattern Classification In Dynamic Environments: Tagged Feature-Class Representation And The Classifiers, Qiuming Zhu
Pattern Classification In Dynamic Environments: Tagged Feature-Class Representation And The Classifiers, Qiuming Zhu
Computer Science Faculty Publications
he classifiers characterized by a tagged feature-class representation, a univariate discrimination approach, a cooperative classification scheme, and a logic-based learning strategy are discussed. Neither of the classifiers bears the constraints to the fixed sets of features and classes. Concepts of the tagged feature-class representation and the properties of feature matching in the dynamic environment are studied. Experimental tests and results of the classifiers are illustrated.