Center-based l₁-clustering method

• Autorzy: Kristian Sabo
• Języki publikacji: EN

Abstrakty

EN

In this paper, we consider the l₁-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the corresponding algorithm is given. The method is illustrated by and compared with some other clustering methods, especially with the l₂-clustering method, which is also known in the literature as a smooth k-means method, on a few typical situations, such as the presence of outliers among the data and the clustering of incomplete data. Numerical experiments show in this case that the proposed l₁-clustering algorithm is faster and gives significantly better results than the l₂-clustering algorithm.

Słowa kluczowe

EN

l₁-clustering; data mining; optimization; weighted median problem

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 1
• Strony: 151-163

Daty

wydano

2014

otrzymano

2013-04-05

poprawiono

2013-07-27

Twórcy

autor

Kristian Sabo

• Department of Mathematics, University of Osijek, Trg Lj. Gaja 6, HR 31 000 Osijek, Croatia

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Identyfikatory

DOI

10.2478/amcs-2014-0012