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A New Hierarchical Clustering Algorithm and Its Performance Validation Using HighDimensional Artificial Data
Riichiro MIZOGUCHI Hirotake NAKASHIMA Osamu KAKUSHO
Publication
IEICE TRANSACTIONS (19761990)
Vol.E66
No.6
pp.359365 Publication Date: 1983/06/25 Online ISSN:
DOI: Print ISSN: 00000000 Type of Manuscript: PAPER Category: Pattern Recoguition Keyword:
Full Text: PDF(492.3KB)>>
Summary:
The present paper has the following three major objectives: a) To propose a new hierarchical clustering algorithm based on a powerful distance measure defined by using knearest neighbor. b) To discuss the validity problem of clustering. c) To describe the need of the constructed data sets for validation of clustering algorithms and to execute the validation of several algorithms including ours using the data sets. Section 2 describes the authors' view of cluster analysis and cluster itself where discussion is made lying stress on the necessity of establishing the concept of a cluster and developing an efficient clustering algorithm free from any restriction associated with the most existing ones. In Sect. 3, a new hierarchical clustering algorithm is proposed which can be considered as a modified version of singlelink algorithm. The algorithm is based on a powerful distance measure defined by using the idea of knearest neighbor. In Sect. 4, some fourdimensional data for testing the validity of clustering algorithms is presented along with how to construct them. Section 5 describes some results of computer simulation of several clustering algorithms including ours using the constructed data. The results show the efficiency of out algorithm.

