A review of clustering techniques and developments

被引：770

作者：

Saxena, Amit ^{[1
]}

Prasad, Mukesh ^{[2
]}

Gupta, Akshansh ^{[3
]}

Bharill, Neha ^{[4
]}

Patel, Om Prakash ^{[4
]}

Tiwari, Aruna ^{[4
]}

Er, Meng Joo ^{[5
]}

Ding, Weiping ^{[6
]}

Lin, Chin-Teng ^{[2
]}

机构：

[1] Guru Ghasidas Vishwavidyalaya, Dept Comp Sci & IT, Bilaspur, India

[2] Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW, Australia

[3] Jawaharlal Nehru Univ, Sch Computat & Integrat Sci, New Delhi, India

[4] Indian Inst Technol Indore, Dept Comp Sci & Engn, Simrol, Madhya Pradesh, India

[5] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[6] Nantong Univ, Sch Comp & Technol, Nantong, Peoples R China

来源：

NEUROCOMPUTING | 2017年 / 267卷

基金：

澳大利亚研究理事会;

关键词：

Unsupervised learning; Clustering; Data mining; Pattern recognition; Similarity measures; FUZZY C-MEANS; UNSUPERVISED FEATURE-SELECTION; NEURAL-NETWORKS; PATTERN-CLASSIFICATION; SPATIAL DATA; ALGORITHMS; OPTIMIZATION; CLASSIFIERS; SCHEME;

D O I：

10.1016/j.neucom.2017.06.053

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：664 / 681

页数：18

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