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MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

Karthikeyani Visalakshi N., Shanthi S., Lakshmi K.
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 23
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa32
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MLA

Karthikeyani Visalakshi N., et al. "MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data." IJCINI vol.15, no.4 2021: pp.1-23. http://doi.org/10.4018/IJCINI.20211001.oa32

APA

Karthikeyani Visalakshi N., Shanthi S., & Lakshmi K. (2021). MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-23. http://doi.org/10.4018/IJCINI.20211001.oa32

Chicago

Karthikeyani Visalakshi N., Shanthi S., and Lakshmi K. "MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-23. http://doi.org/10.4018/IJCINI.20211001.oa32

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Abstract

Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.