A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework

Satish Chander, P. Vijaya, Praveen Dhyani

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297034

MLA

Chander, Satish, et al. "A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework." IJSWIS vol.18, no.1 2022: pp.1-25. http://doi.org/10.4018/IJSWIS.297034

APA

Chander, S., Vijaya, P., & Dhyani, P. (2022). A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-25. http://doi.org/10.4018/IJSWIS.297034

Chicago

Chander, Satish, P. Vijaya, and Praveen Dhyani. "A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-25. http://doi.org/10.4018/IJSWIS.297034

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Abstract

This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.