Optimized k-means clustering with intelligent initial centroid selection for web search using URL and tag contents

Poomagal, S.; Hamsapriya, T.

doi:10.1145/1988688.1988764

Cited by 2 publications

(1 citation statement)

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“…Most of clustering algorithm such as K-Means, is more concern with measuring closest distance of objects region based on their multiple features space of coordinate location [9][10]. However, considering ranking based application context, ranking level among objects are represented based on their ordinal features.…”

Section: Related Workmentioning

confidence: 99%

A hybrid model of ordinal ranking-based clustering using G+Rank K-Means

Suhailan¹,

Samad²,

Burhanuddin³

et al. 2018

IJET

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K-Means is a clustering technique that maps object features onto multidimensional coordinates and groups them based on location closeness. However, measuring closest distance can be doubtful when ranking representation of ordinal scale objects are not considered. Thus, distribution of objects in a cluster may violate ranking representation. For example, a same-rank object may be grouped into different clusters. To address this issue, an enhanced of K-Means algorithm is proposed to achieve better and meaningful result of ranking-based clustering. It is based on integration of ranking algorithm that sort objects into ranking list which also representing object closeness based on its nearby location. A new additional step in K-Means is proposed in reassigning unaligned K-Means nearest objects using ranking attribute that eventually accelerates the clustering process. AHP ranking algorithm is integrated into K-Means in achieving a ranking-based cluster. This enhancement was evaluated on three ordinal datasets covering 67 Java programs, 92 students' marks on computer architecture subject and 456 EUFA's football club coefficient ranking list. The results show that by integrating ranking algorithm in K-Means as proposed in G + Rank K-Means, a rank cluster representation has been successfully achieved. The purity value that represents the correctness against certain group classification has also increased.

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Section: Related Workmentioning

confidence: 99%