Welcome to the InfoSci Platform
Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set

Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set

Dhayanithi Jaganathan, Akilandeswari Jeyapal
Copyright: © 2020 |Pages: 14
ISBN13: 9781522599029|ISBN10: 1522599029|ISBN13 Softcover: 9781522599036|EISBN13: 9781522599043
DOI: 10.4018/978-1-5225-9902-9.ch004
Cite Chapter Cite Chapter

MLA

Jaganathan, Dhayanithi, and Akilandeswari Jeyapal. "Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set." Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, IGI Global, 2020, pp. 63-76. https://doi.org/10.4018/978-1-5225-9902-9.ch004

APA

Jaganathan, D. & Jeyapal, A. (2020). Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set. In S. Velayutham (Ed.), Handbook of Research on Applications and Implementations of Machine Learning Techniques (pp. 63-76). IGI Global. https://doi.org/10.4018/978-1-5225-9902-9.ch004

Chicago

Jaganathan, Dhayanithi, and Akilandeswari Jeyapal. "Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set." In Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, 63-76. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9902-9.ch004

Export Reference

Mendeley
Favorite

Abstract

In recent days, researchers are doing research studies for clustering of data which are heterogeneous in nature. The data generated in many real-world applications like data form IoT environments and big data domains are heterogeneous in nature. Most of the available clustering algorithms deal with data in homogeneous nature, and there are few algorithms discussed in the literature to deal the data with numeric and categorical nature. Applying the clustering algorithm used by homogenous data to the heterogeneous data leads to information loss. This chapter proposes a new genetically-modified k-medoid clustering algorithm (GMODKMD) which takes fused distance matrix as input that adopts from applying individual distance measures for each attribute based on its characteristics. The GMODKMD is a modified algorithm where Davies Boudlin index is applied in the iteration phase. The proposed algorithm is compared with existing techniques based on accuracy. The experimental result shows that the modified algorithm with fused distance matrix outperforms the existing clustering technique.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.