A comparative investigation of approaches for web search results clustering. (8th May 2020)

• Record Type: Journal Article
• Authors: Salah, Zaher; Al-Ghuwairi, Abdel-Rahman; Aloqaily, Ahmad; Baarah, Aladdin; Alsarhan, Ayoub
• Is Part Of: International journal of advanced intelligence paradigms. Volume 17:Number 3/4(2020)
• Page Start: 342
• Page End: 366
• Publication Date: 2020-05-08
• Subjects: information retrieval -- machine learning -- text mining -- web search results clustering -- WSRC; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

The huge diversity of topics covered by the online textual documents is expected as these documents are originated from various resources and cover various topics. Looking to all of these aspects, how to search and find specific documents relative to a specific topic in the user mind and how to facilitate the browsing process? And how to reflect properly the user intention to the information retrieval system to perform the searching and delivering task in precise and fast process? This paper investigates various techniques used for clustering the web search results in order to meet that user's information needs. The goal is not to facilitate finding specific documents only but also to make it easier to preview the general structure and distribution of the topics and to reveal hidden topics in the corpus. The aim of this paper is to provide the reader with the relevant background in much more detail.