A New Ensemble Clustering Approach for Effective Information Retrieval

Archana Maruthavanan and Ayyasamy Ayyanar
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Archana Maruthavanan: Allagappa Government Polytechnic College, Department of Computer Engineering
Ayyasamy Ayyanar: Government Polytechnic College, Department of Computer Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1455-1464 from Springer

Abstract: Abstract Information retrieval systems are those systems which work upon some set of searching algorithms that enables the system to retrieve the desired information from the system. There are several techniques that are already available like, algorithms based on directed trees, fuzzy clustering algorithm, and several divisive algorithms. Different algorithms provides partitioned results, but the ensemble clustering combines the multiple partitioned results and provide a better result to the user. The motive behind combination of multiple partitions is to enhance the quality of each every cluster and improving the service of the retrieval system. For this purpose, we introduce a new ensemble approach for effective information retrieval through clustering process over the documents or online contents. Ensemble clustering creates several smaller data clusters from a big data cluster and then transforms those clusters into consensus matrix form which results into very efficient and better performance.

Keywords: Fuzzy clustering; Partitioned results; Ensemble clustering; and Consensus matrix (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_149

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DOI: 10.1007/978-3-030-41862-5_149

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