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2008

Incremental clustering of mixed data based on distance hierarchy

9 years 1 months ago
Incremental clustering of mixed data based on distance hierarchy
Clustering is an important function in data mining. Its typical application includes the analysis of consumer's materials. Adaptive resonance theory network (ART) is very popular in the unsupervised neural network. Type I adaptive resonance theory network (ART1) deals with the binary numerical data, whereas type II adaptive resonance theory network (ART2) deals with the general numerical data. Several information systems collect the mixing type attitudes, which included numeric attributes and categorical attributes. However, ART1 and ART2 do not deal with mixed data. If the categorical data attributes are transferred to the binary data format, the binary data do not reflect the similar degree. It influences the clustering quality. Therefore, this paper proposes a modified adaptive resonance theory network (M-ART) and the conceptual hierarchy tree to solve similar degrees of mixed data. This paper utilizes artificial simulation materials and collects a piece of actual data about t...
Chung-Chian Hsu, Yan-Ping Huang
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where ESWA
Authors Chung-Chian Hsu, Yan-Ping Huang
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