A clustering algorithm based on an estimated distribution model

L Tan; D Taniar; K A Smith

A clustering algorithm based on an estimated distribution model

L Tan, D Taniar, K A Smith

Research output: Contribution to journal › Article › peer-review

Abstract

This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This sheds light on how much a cluster metric can play in clustering convergence.

Original language	English
Journal	International Journal of Business Intelligence and Data Mining
Volume	1
Issue number	2
Publication status	Published - 2005
Externally published	Yes

Keywords

Clustering algorithms
Clustering convergence
Clustering metric
Conditional merging
Conditional splitting
Convergence space
Data mining
Estimated distribution algorithms
Maximum entropy

Disciplines

Educational Assessment, Evaluation, and Research

Access to Document

http://inderscience.metapress.com/content/adtgmj50u9r2khv8/

Cite this

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title = "A clustering algorithm based on an estimated distribution model",

abstract = " This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This sheds light on how much a cluster metric can play in clustering convergence.",

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AU - Tan, L

AU - Taniar, D

AU - Smith, K A

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N2 - This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This sheds light on how much a cluster metric can play in clustering convergence.

AB - This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This sheds light on how much a cluster metric can play in clustering convergence.

KW - Clustering algorithms

KW - Clustering convergence

KW - Clustering metric

KW - Conditional merging

KW - Conditional splitting

KW - Convergence space

KW - Data mining

KW - Estimated distribution algorithms

KW - Maximum entropy

M3 - Article

VL - 1

JO - International Journal of Business Intelligence and Data Mining

JF - International Journal of Business Intelligence and Data Mining

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ER -