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Influence angel cluster approach for data clustering

Aziz, Nazrina and Dong, Qian Wang (2010) Influence angel cluster approach for data clustering. In: 2nd International Conference on Mathematical Sciences (ICMS2 2010), 30 November - 3 December 2010 , Putra World Trade Centre (PWTC) Kuala Lumpur, Malaysia . (Unpublished)

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Clustering allows one to handle a large data set effectively. It is a technique for solving classification problems. There are two major challenges in clustering. First, identifying clusters in high-dimensional data sets is a difficult task because of the curse of dimensionality. Second, a new dissimilarity measures is needed as some traditional distance functions cannot capture the pattern dissimilarity among the objects. This article interested in the latter challenge. Notice that data measures are very important steps in clustering. There are many types of data measurement that deal with continuous,categorical or mixed variables. This article proposed an alternative measurement, called Influence Angle Cluster Approach (iaca). The iaca was developed based on eigenstructure of the covariance matrix. The proposed measurement able to identify cluster of observation and it also has the ability to handle a data set with mixed variables. Apart from developing a cluster for a data set, this study also measure whether or not the proposed IACA have constructed either a strong or reasonable clustering structure by using silhouette index.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Jointly organized by School of Mathematical Sciences, Universiti Kebangsaan Malaysia, Malaysian Mathematical Sciences Society, and University of Alba Iulia from Romania
Uncontrolled Keywords: Influence angle; eigenvector and eigenvalue; partitional method; deletion measures; dissimilarity matrix; silhouette index
Subjects: Q Science > QA Mathematics
Divisions: College of Arts and Sciences
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 20 Feb 2011 08:50
Last Modified: 20 Feb 2011 08:50

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