NDLI: SKIFF: Spherical K-means with iterative feature filtering for text document clustering

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SKIFF: Spherical K-means with iterative feature filtering for text document clustering

Content Provider	SAGE Publishing
Author	Sharma, Iti Sharma, Abhay Chaturvedi, Rekha Rajpurohit, Jitendra Kumar, Manoj
Copyright Year	2023
Abstract	Text clustering has been an overlooked field of text mining that requires more attention. Several applications require automatic text organisation which relies on an information retrieval system based on organised search results. Spherical k-means is a successful adaptation of the classic k-means algorithm for text clustering. However, conventional methods to accelerate k-means may not apply to spherical k-means due to the different nature of text document data. The proposed work introduces an iterative feature filtering technique that reduces the data size during the process of clustering which further produces more feature-relevant clusters in less time compared to classic spherical k-means. The novelty of the proposed method is that feature assessment is distinct from the objective function of clustering and derived from the cluster structure. Experimental results show that the proposed scheme achieves computation speed without sacrificing cluster quality over popular text corpora. The demonstrated results are satisfactory and outperform compared to recent works in this domain.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/01655515231165230?download=true
ISSN	01655515
Journal	Journal of Information Science (JIS)
e-ISSN	17416485
DOI	10.1177/01655515231165230
Language	English
Publisher	Sage Publications UK
Publisher Date	2023-05-02
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2023
Subject Keyword	text clustering Document clustering feature selection spherical K-means text mining
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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