Universiti Teknologi Malaysia Institutional Repository

Clustering technique in data mining : general and research perspective.

Che Mat @ Mohd. Shukor, Zamzarina and Md. Sap, Mohd. Noor (2002) Clustering technique in data mining : general and research perspective. Jurnal Teknologi Maklumat, 14 (2). pp. 50-63. ISSN 0128-3790



As the amount and dimensionality of data grows beyond the grasp of human minds, automation of pattern discovery becomes crucial. One of the most popular techniques to extract pattern and knowledge from large amount of data in databases is data mining. Data mining can be defined as process of searching the particular patterns and relationship from large amount of data in databases using sophisticated data analysis tools and techniques to build models that may be used to make valid predictions. One of the existing data mining techniques is clustering. Clustering in data mining is a discovery process that groups a set of data such that the intra-cluster similarity is maximized and inter-cluster similarity is minimizes. These discovered clusters are used to explain the characteristics of the data distribution. This paper present most popular clustering technique such as hierarchical clustering and partitional clustering, cluster selection schemes, clustering criterion functions, assessing cluster quality and conclusion.

Item Type:Article
Uncontrolled Keywords:clustering, data mining, unsupervised learning, descriptive learning, partitioning, hierarchical clustering, agglomerative
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:8550
Deposited By: Zalinda Shuratman
Deposited On:11 May 2009 02:36
Last Modified:01 Nov 2017 04:17

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