Open AccessExtensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey
Author(s) -
Yalamarthi Leela Sandhya Rani,
V. Sucharita,
K. Satyanarayana
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i4.pp2351-2357
Subject(s) - cluster analysis , computer science , data mining , consensus clustering , scalability , robustness (evolution) , cluster (spacecraft) , variety (cybernetics) , class (philosophy) , data science , machine learning , artificial intelligence , fuzzy clustering , cure data clustering algorithm , database , biochemistry , chemistry , gene , programming language
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.