Emblematic Fuzzy C-means Clustering for Demographic Dataset
Journal Title: International Journal of Computer Science & Engineering Technology - Year 2014, Vol 5, Issue 8
Abstract
Clustering algorithms are very useful in the field of marketing, education, and healthcare and engineering. It has several advantages such as -it deals with different type of data (text, numerical, image, categorical.),handle outliers, fuzzy data and noise, discover the irregular shape of clusters, produces the result that are easily understandable and insensitive to order of input data. This research work proposed an Emblematic Fuzzy C Means algorithm and analyzed its performance on demographic data set which contains 64 countries and 23 attributes and is taken from World Health Organization. For grouping the countries two attributes Human development index (HDI) and control of corruption is taken into consideration. HDI is the comparative measure of health, education and life of expectancy. Control of corruption defines the economical growth. A comparative analysis of conventional Fuzzy C Means (FCM) and Emblematic Fuzzy C Means (EFCM) has been performed to find out best cluster size with maximum performance validity index.
Authors and Affiliations
Ruchi Arya
Keywords
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