The Seeding Algorithm for Spherical k-Means Clustering with Penalties

Sai Ji,Longkun Guo,Dongmei Zhang,Min Li,Dachuan Xu

doi:10.1007/978-3-030-27195-4_14

The Seeding Algorithm for Spherical k-Means Clustering with Penalties

Sai Ji, Longkun Guo + Show 3 more

https://doi.org/10.1007/978-3-030-27195-4_14

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Publication Date: Jan 1, 2019

Citations: 1

Affiliation: Beijing University of Technology, Fuzhou University, Shandong Jianzhu University, Shandong Normal University

#Spherical K-means Clustering #Spherical Clustering + Show 8 more

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Abstract

Spherical k-means clustering is a generalization of k-means problem which is NP-hard and has widely applications in data mining. It aims to partition a collection of given data with unit length into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In this paper, we introduce the spherical k-means clustering with penalties and give a \(2\max \{2,M\}(1+M)(\ln k+2)\)-approximate algorithm, where M is the ratio of the maximal and the minimal penalty values of the given data set.

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