Open AccessFeature selection, optimization and clustering strategies of text documents
Author(s) -
A. Kousar Nikhath,
K. Subrahmanyam
Publication year - 2019
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.v9i2.pp1313-1320
Subject(s) - cluster analysis , computer science , document clustering , data mining , context (archaeology) , categorical variable , brown clustering , task (project management) , selection (genetic algorithm) , search engine indexing , conceptual clustering , correlation clustering , consensus clustering , clustering high dimensional data , artificial intelligence , categorization , cure data clustering algorithm , machine learning , geography , management , economics , archaeology
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.