A Fast Incremental Clustering Algorithm

被引：0

作者：

Su, Xiaoke ^{[1
]}

Lan, Yang ^{[2
]}

Wan, Renxia ^{[1
]}

Qin, Yuming ^{[1
]}

机构：

[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China

[2] Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Henan, Peoples R China

来源：

ISIP: 2009 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING, PROCEEDINGS | 2009年

基金：

中国国家自然科学基金;

关键词：

incremental clustering; categorical data; radius threshold value; inter-cluster dissimilarity measure; clustering accuracy; data mining;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Clustering has played a very important role in data mining. In this paper, a fast incremental clustering algorithm is proposed by changing the radius threshold value dynamically. The algorithm restricts the number of the final clusters and reads the original dataset only once. At the same time an inter-cluster dissimilarity measure taking into account the frequency information of the attribute values is introduced. It can be used for the categorical data. The experimental results on the mushroom dataset show that the proposed algorithm is feasible and effective. It can be used for the large-scale data set.

引用

页码：175 / +

页数：2

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