A clustering technique using single pass clustering algorithm for search engine

Indra, Z. and Zamin, N. and Jaafar, J. (2014) A clustering technique using single pass clustering algorithm for search engine. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Internet users rely heavily on search engine to explore and find useful information buried in the websites. Up to now, the search results returned by the search engines are still far from satisfaction due to a long list of search results which in practice contains a mix of relevant and irrelevant information. The manual process of filtering the irrelevant information is daunting and time consuming. Clustering is one of the popular solutions for this cumbersome task. However, our literature studies revealed that research on document clustering for Asian languages are relatively limited as compared to English. Whilst the application of document clustering technique in search engines is commonly less available. In this research, a clustering technique for search engine using Single Pass Clustering (SPC) Algorithm is proposed. The technique is experimented on a set of Indonesian news documents to support the limited research of document clustering for Indonesian language. An experiment done on 200 Indonesian news documents has produced a number of satisfactory labelled clusters and the application of the algorithm is shown on a simulated search engine. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 2; Conference of 2014 4th World Congress on Information and Communication Technologies, WICT 2014 ; Conference Date: 8 December 2014 Through 11 December 2014; Conference Code:111785
Uncontrolled Keywords: Algorithms; Cluster analysis; Computational linguistics; Information filtering; Information retrieval; Search engines; Text processing, Clustering documents; Clustering techniques; Cosine similarity; Document Clustering; Indonesian languages; Literature studies; Single-pass clustering; Term frequencyinverse document frequency (TF-IDF), Clustering algorithms
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:15
Last Modified: 09 Nov 2023 16:15
URI: https://khub.utp.edu.my/scholars/id/eprint/4317

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