A Clustering Algorithm Based on Variance-Similarity

Abstract:

Clustering algorithms, like K-means Algorithm, use distances in attribute space to cluster data. However the computation of distances in attribute space influences the accuracy. So innovatively, Variance-Similarity clustering algorithm defines similarity as a function of the attribute variance, and clusters data by the comparison of similarities. In computer simulation, the comparison of Variance-Similarity Algorithm and K-means Algorithm on UCI data sets presents that Variance-Similarity Algorithm has a better clustering accuracy than K-means Algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1306-1309
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1306
• Online since: July 2013
• Authors: Zhen Dong Li, Fei Li
• Keywords: Attribute Values, Clustering Algorithm, Data Mining (DM), Variance-Similarity

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