Optimizing K-Means initial number of cluster based  heuristic approach: literature review analysis  perspective

Muhammad Modi Lakulu

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Type :	article
Subject :	L Education (General)
Main Author :	Muhammad Modi Lakulu
Additional Authors :	Ramlah Mailok Harunur Rosyid
Title :	Optimizing K-Means initial number of cluster based heuristic approach: literature review analysis perspective

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris

One popular clustering technique - the K-means widely use in educational scope to clustering and mapping document, data, and user performance in skill. K-means clustering is one of the classical and most widely used clustering algorithms shows its efficiency in many traditional applications its defect appears obviously when the data set to become much more complicated. Based on some research on K-means algorithm shows that Number of a cluster of K-means cannot easily be specified in much realworld application, several algorithms requiring the number of cluster as a parameter cannot be effectively employed. The aim of this paper describes the perspective K-means problems underlying research. Literature analysis of previous studies suggesting that selection of the number of clusters randomly cause problems such as suitable producing globular cluster, less efficient if as the number of cluster grow K-means clustering becomes untenable. From those literature reviews, the heuristic optimization will be approached to solve an initial number of cluster randomly.

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