Fuzzy-Based Approach for Clustering Data with Multivalued Features

Author:

Prakash K L. N. C.1ORCID,Vimaladevi M.2ORCID,Chakravarthy V. Deeban3ORCID,Narayana G. Surya4ORCID,Srinivasulu Asadi5ORCID

Affiliation:

1. Department of Computer Science and Engineering, CVR College of Engineering, Mangalpalli Hyderabad, India

2. Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

3. Department of Computing Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

4. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

5. BlueCrest University, Liberia

Abstract

In analysis of data, objects have mostly been characterized by a set of characteristics known as attributes, which together contained only one value for each object. Besides that, a few attributes in reality could include with more than a single value; such as from a human beside multiple profession characterizations, practises, communication methods, and capabilities, in addition to shipping addresses, of that kind of attributes are referred to as multivalued attributes and are typically regarded as null attributes when data is processed employing machine learning procedures. Throughout this article, another similarity mechanism is introduced that is defined around including multivalued characteristics which can be used for grouping. We propose a model to analyse each factor’s relative prominence for different data collection challenges in order to enable the selection among the most suited multivalued elements. The suggested methodology is a clustering technique for development and evolution that employs fuzzy c-means clustering and retains the new and more effective membership component by implementing the proposed similarity metric. Clustering of multivalued variables using fuzzy c-means is the efficient grouping criteria that results; any methodology to group-related data appears viable. The results show that our assessment not only improves previous segmentation methods on the multivalued cluster-based architecture but also helps in the improvement of the standard similarity metrics.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Reference40 articles.

1. A clustering technique for summarizing multivariate data

2. Some methods for classification and analysis of multivariate observations;J. B. Mac Queen

3. A new approach to clustering

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