Welcome to the InfoSci Platform
Reference Hub1
Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

Deepali Virmani, Nikita Jain, Ketan Parikh, Shefali Upadhyaya, Abhishek Srivastav
Copyright: © 2018 |Volume: 8 |Issue: 1 |Pages: 18
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781522544661|DOI: 10.4018/IJKDB.2018010104
Cite Article Cite Article

MLA

Virmani, Deepali, et al. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology." IJKDB vol.8, no.1 2018: pp.42-59. http://doi.org/10.4018/IJKDB.2018010104

APA

Virmani, D., Jain, N., Parikh, K., Upadhyaya, S., & Srivastav, A. (2018). Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 42-59. http://doi.org/10.4018/IJKDB.2018010104

Chicago

Virmani, Deepali, et al. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 42-59. http://doi.org/10.4018/IJKDB.2018010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.

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.