MO - Mineclust: A Framework for Multi-objective Clustering

被引:2
|
作者
Fisset, Benjamin [1 ]
Dhaenens, Clarisse [1 ,2 ]
Jourdan, Laetitia [1 ,2 ]
机构
[1] Inria Lille Nord Europe, DOLPHIN Project Team, F-59650 Villeneuve Dascq, France
[2] Univ Lille 1, CRIStAL, UMR CNRS 9189, F-59650 Villeneuve Dascq, France
关键词
ALGORITHM;
D O I
10.1007/978-3-319-19084-6_30
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article presents MO - Mine(clust) a first package of the platform in development MO - Mine. This platform aims at providing optimization algorithms, and in particular multi-objective approaches, to deal with classical datamining tasks (Classification, association rules...). This package MO - Mineclust is dedicated to clustering. Indeed, it is well-known that clustering may be seen as a multi-objective optimization problem as the goal is both to minimize distances between data belonging to a same cluster, while maximizing distances between data belonging to different clusters. In this paper we present the framework as well as experimental results, to attest the benefit of using multi-objective approaches for clustering.
引用
收藏
页码:293 / 305
页数:13
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