Application of Clustering for Improving Search Result of a Website

Mehrotra, Shashi; Kohli, Shruti

doi:10.1007/978-81-322-2752-6_34

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Some of the direct areas of clustering application generally discussed in the literature have been textual document classification, image segmentation, object recognition, character recognition, information retrieval, data mining, spatial data analysis, business analytics, data reduction, and big data mining. Other areas indicated by Saxena et al ( 2017 ), have been sequence analysis (Durbin et al 1998 ; Li et al 2012 ), human genetic clustering, (Kaplan and Winther 2013 ; Lelieveld et al 2017 ; Marbac et al 2019 ), mobile banking and information system (Motiwalla et al 2019 ; Shiau et al 2019 ), social network analysis (Scott and Carrington 2011 ; Shiau et al 2017 ; Khamparia et al 2020 ), search result grouping (Mehrotra and Kohli 2016 ; Kohli and Mehrotra 2016 ), software evolution (Rathee and Chhabra 2018 ; Izadkhah and Tajgardan 2019 ), recommender systems (Petwal et al 2020 ), educational data mining (Baker 2010 ; Guleria and Sood 2020 ), climatology (Sharghi et al 2018 ; Pike and Lintner 2020 ; Chattopadhyay et al 2020 ) and robotics (Khouja and Booth 1995 ; Zhang et al 2013 ). In Table 6 below we briefly discuss a few applications as indicated by Saxena et al ( 2017 ) and also provide references for more detailed studies.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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“…According to them, various challenges affect clustering, including selecting the number of clusters, type of data in the database like nominal and binary and identifying distance of measure. They presented various tools for clustering to overcome these challenges, including search result clustering, topical clustering of search result and keyword similarity measure tool [11]. Doulamis et al [12] acknowledges that the expansion of social networks, especially Twitter, has stimulated an interest in cultural heritage based on the enormous sharing of images and comments by users.…”

Section: English Document Clusteringmentioning

confidence: 99%

“…Kohli and Mehrotra [11] undertook an experiment focusing on clustering website elements to improve search results. The researchers recommend the use of clustering techniques to group website elements.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

2021

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The internet has become one of the main sources of news spread as it unleashed the information dissemination space, where the news websites express opinions on entities while also reporting on recent or unusual security risks. Recently, many research studies have focused on sentimental reflection on the views and impressions of people utilising natural language processing and analytical linguistics. Therefore, we have collected corpus from popular Arabic websites that publish articles related to recent security issues, and we provide light weight preprocessing techniques where data is term matrix is transformed. We also present an intensive lexical-driven data analysis with visualised data views, as our topic modelling technique can effectively extract significant topics from all the collected text from different websites. Our experiments validate the k-means clustering algorithm with and without the latent Dirichlet allocation topic modelling method, and we adopted various validation techniques to measure the topic clustering internally and externally. As shown in the experiments' results, our proposed combined method has a high round index rate of 87.2%, with a large number of topics and clusters. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Enhance Incremental Clustering for Time Series Datasets Using Distance Measures

Khobragade

Mulay

2017

Advances in Intelligent Systems and Computing

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Application of Clustering for Improving Search Result of a Website

Cited by 6 publications

References 14 publications

Data clustering: application and trends

Data clustering: application and trends

Enhancing topic clustering for Arabic security news based on k‐means and topic modelling

Enhance Incremental Clustering for Time Series Datasets Using Distance Measures

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