research-article

Enhanced clustering models with wiki-based k-nearest neighbors-based representation for web search result clustering

Authors:

Ali Sabah Abdulameer,

Nor Samsiah Sani,

Adil Yaseen TahaAuthors Info & Claims

Volume 34, Issue 3

Pages 840 - 850

https://doi.org/10.1016/j.jksuci.2020.02.003

Published: 01 March 2022 Publication History

Abstract

Information retrieval is a difficult process due to the overabundance of information on the web. Nowadays, search result responds to user queries with too many results although only a few are relevant. Therefore, the existing clustering methods that fail in clustering snippets (short texts) of web documents due to the low frequencies of document terms should be deeply investigated. One of the approaches that can be used to solve this problem is the expansion of document terms with semantically similar terms. Hence, a list of terms with their closest and accurate semantically similar words (word representation) must be built. This study aims to design and develop a new framework to enhance the performance of web search result clustering (WSRC). The research also presents a new unsupervised distributed word representation scheme where each word is represented by a vector of its semantically related words; such as scheme expands snippets and user queries. The proposed framework consists of several activities, such as (1) various standard datasets (Open Directory Project [ODP]-239 and MORESQUE) that are used for evaluating search result clustering algorithms for most cited dataset works, (2) text pre-processing, (3) document representation based on a new wiki-based k-nearest neighbors (KNN) representation method, (4) effect of the proposed model on the performance of traditional clustering methods (k-means, k-medoids, single-linkage, and complete-linkage) for WSRC, and (5) evaluation stage of the proposed method. Results indicate that enhanced clustering methods, according to the new wiki-KNN based representation method in comparison with the baseline methods, show a significant improvement in WSRC. Furthermore, the new data representation scheme has enhanced the overall performance of clustering methods.

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Cited By

Yan L(2023)A Low-Complexity Channel Estimation in Internet of Vehicles in Intelligent Transportation Systems for 5G CommunicationJournal of Organizational and End User Computing10.4018/JOEUC.32675935:1(1-21)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.4018/JOEUC.326759

Index Terms

Enhanced clustering models with wiki-based k-nearest neighbors-based representation for web search result clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

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cover image Journal of King Saud University - Computer and Information Sciences

Journal of King Saud University - Computer and Information Sciences Volume 34, Issue 3

Mar 2022

505 pages

ISSN:1319-1578

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Elsevier Science Inc.

United States

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Published: 01 March 2022

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Cited By

Yan L(2023)A Low-Complexity Channel Estimation in Internet of Vehicles in Intelligent Transportation Systems for 5G CommunicationJournal of Organizational and End User Computing10.4018/JOEUC.32675935:1(1-21)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.4018/JOEUC.326759

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