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Author:

Yuan, Fan (Yuan, Fan.) | Xu, Da-Chuan (Xu, Da-Chuan.) | Du, Dong-Lei (Du, Dong-Lei.) | Zhang, Dong-Mei (Zhang, Dong-Mei.)

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EI Scopus

Abstract:

We study a problem called the k-means problem with penalties (k-MPWP), which is a natural generalization of the typical k-means problem. In this problem, we have a set D of client points in Rd, a set F of possible centers in Rd, and a penalty cost pj>0 for each point j∈D. We are also given an integer k which is the size of the center point set. We want to find a center point set S⊆F with size k, choose a penalized subset of clients P⊆D, and assign every client in D\P to its open center. Our goal is to minimize the sum of the squared distances between every point in D\P to its assigned centre point and the sum of the penalty costs for all clients in P. By using the multi-swap local search technique and under the fixed-dimensional Euclidean space setting, we present a polynomial-time approximation scheme (PTAS) for the k-MPWP. © Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Keyword:

Geometry Approximation algorithms K-means clustering Polynomial approximation Local search (optimization)

Author Community:

  • [ 1 ] [Yuan, Fan]Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Xu, Da-Chuan]Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Du, Dong-Lei]Faculty of Management, University of New Brunswick, Fredericton; NB; E3B 9Y2, Canada
  • [ 4 ] [Zhang, Dong-Mei]School of Computer Science and Technology, Shandong Jianzhu University, Shandong; Jinan 250101, China

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Source :

Journal of the Operations Research Society of China

ISSN: 2194-668X

Year: 2024

Issue: 2

Volume: 12

Page: 351-362

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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