A Survey of Multiobjective Evolutionary Clustering
A Survey of Multiobjective Evolutionary Clustering
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DOI：
10.1145/2742642
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发表时间：
2015-07-01
影响因子：
16.6
通讯作者：
Bandyopadhyay, Sanghamitra
中科院分区：
文献类型：
--
作者：
Mukhopadhyay, Anirban;Maulik, Ujjwal;Bandyopadhyay, Sanghamitra
数据聚类是一种流行的无监督数据挖掘工具，用于根据某些相似性/不相似性度量将给定的数据集划分为同质组。传统的聚类算法往往事先假设的集群结构，并采取相应的合适的目标函数，通过经典的技术或元启发式方法进行优化。众所周知，当集群假设在数据中不成立时，这些算法的性能会很差。多目标聚类，其中多个目标函数同时优化，已成为一个有吸引力的和强大的替代方案，在这种情况下。特别是，应用多目标进化算法聚类已经成为流行，在过去的十年中，因为他们的人口为基础的性质。在这里，我们提供了一个全面的和批判性的调查文献中存在的众多多目标进化聚类技术。根据采用的编码策略、目标函数、进化算子、保持非支配解的策略以及最终解的选择方法，对这些技术进行了分类。提到了不同方法的利弊。最后，我们讨论了多目标聚类在图像分割、生物信息学、Web挖掘等领域的实际应用。
Data clustering is a popular unsupervised data mining tool that is used for partitioning a given dataset into homogeneous groups based on some similarity/dissimilarity metric. Traditional clustering algorithms often make prior assumptions about the cluster structure and adopt a corresponding suitable objective function that is optimized either through classical techniques or metaheuristic approaches. These algorithms are known to perform poorly when the cluster assumptions do not hold in the data. Multiobjective clustering, in which multiple objective functions are simultaneously optimized, has emerged as an attractive and robust alternative in such situations. In particular, application of multiobjective evolutionary algorithms for clustering has become popular in the past decade because of their population-based nature. Here, we provide a comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature. The techniques are classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. The pros and cons of the different approaches are mentioned. Finally, we have discussed some real-life applications of multiobjective clustering in the domains of image segmentation, bioinformatics, web mining, and so forth.