A new cluster validity index using maximum cluster spread based compactness measure

Wani, M. Arif; Riyaz, Romana

doi:10.1108/ijicc-02-2016-0006

Cited by 16 publications

(8 citation statements)

References 38 publications

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“…Moreover, the authors have considered dual clustering issues so as to reduce the number of clusters. Various clustering schemes are proposed to improve search in different ways (Kumar, 2011;Wani and Riyaz, 2016).…”

Section: Literature Surveymentioning

confidence: 99%

An incremental learning approach for the text categorization using hybrid optimization

Kayest

Jain

2019

IJICC

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Purpose Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents. Design/methodology/approach This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm. Findings The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively. Originality/value The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.

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Section: Literature Surveymentioning

confidence: 99%

An incremental learning approach for the text categorization using hybrid optimization

Kayest

Jain

2019

IJICC

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“…Our empirical evaluation measures mostly depend on metrics requiring no initial labeling of data. We used internal clustering stability measures to evaluate the internal cluster model stability ( Wani & Riyaz, 2016 ), and clustering accuracy based on the judgment of the human experts ( Ruotsalo et al, 2018 ). We obtained accuracy and stability scores by dispatching pre-defined queries on Google’s real dataset.…”

Section: Resultsmentioning

confidence: 99%

An architecture for non-linear discovery of aggregated multimedia document web search results

Khan¹,

Rashid²,

Saleem³

et al. 2021

PeerJ Computer Science

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The recent proliferation of multimedia information on the web enhances user information need from simple textual lookup to multi-modal exploration activities. The current search engines act as major gateways to access the immense amount of multimedia data. However, access to the multimedia content is provided by aggregating disjoint multimedia search verticals. The aggregation of the multimedia search results cannot consider relationships in them and are partially blended. Additionally, the search results’ presentation is via linear lists, which cannot support the users’ non-linear navigation patterns to explore the multimedia search results. Contrarily, users’ are demanding more services from search engines. It includes adequate access to navigate, explore, and discover multimedia information. Our discovery approach allow users to explore and discover multimedia information by semantically aggregating disjoint verticals using sentence embeddings and transforming snippets into conceptually similar multimedia document groups. The proposed aggregation approach retains the relationship in the retrieved multimedia search results. A non-linear graph is instantiated to augment the users’ non-linear information navigation and exploration patterns, which leads to discovering new and interesting search results at various aggregated granularity levels. Our method’s empirical evaluation results achieve 99% accuracy in the aggregation of disjoint search results at different aggregated search granularity levels. Our approach provides a standard baseline for the exploration of multimedia aggregation search results.

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“…Chou et al [43] presented an area measure to evaluate the initial cluster number based on the information of cluster areas. Wani and Riyaz [44] presented a new compactness measure using a novel penalty function to describe the typical behavior of a cluster. Azhagiri and Rajesh [45] proposed a novel approach to measure the quality of the cluster and can find intrusions using intrusion unearthing and probability clomp algorithm.…”

Section: Related Workmentioning

confidence: 99%

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Zhang

Kou

et al. 2020

Complexity

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In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

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A new cluster validity index using maximum cluster spread based compactness measure

Cited by 16 publications

References 38 publications

An incremental learning approach for the text categorization using hybrid optimization

An incremental learning approach for the text categorization using hybrid optimization

An architecture for non-linear discovery of aggregated multimedia document web search results

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

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