A review of clustering techniques and developments

NEUROCOMPUTING

卷:267

Review

Saxena, Amit¹  Prasad, Mukesh²  Gupta, Akshansh³  Bharill, Neha⁴  Patel, Om Prakash⁴  Tiwari, Aruna⁴  Er, Meng Joo⁵  Ding, Weiping⁶  Lin, Chin-Teng² 

[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

关键词: Unsupervised learning;    Clustering;    Data mining;    Pattern recognition;    Similarity measures;   

DOI  :  10.1016/j.neucom.2017.06.053

来源: Elsevier

【 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.

【 授权许可】

Free