2018
DOI: 10.26483/ijarcs.v9i2.5881
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Abstract: Data mining is a collection of methods used to extract useful information from large data bases. Cluster Analysis refers to the grouping of a set of data points into clusters. Most widely used partitioning methods are K-means and Fuzzy c-means (FCM) algorithms. However, they suffer from the difficulties such as random selection of initial centre values and handling outlier data points. Most of the existing clustering methods use the Euclidean distance metric. The modified fuzzy c-means algorithm (MFCM) is effi… Show more

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Cited by 2 publications
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“…Typically, it is applied to numeric and continuous data. It is commonly used in wireless sensor networks, pattern recognition, document classification, rideshare data analysis and diagnostic systems (Jafar, 2018). The advantages of k-means clustering are that it is relatively simple to perform, assemble stable and tight clusters, converges after some interactions and low computational cost (Chauhan & Gupta, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Typically, it is applied to numeric and continuous data. It is commonly used in wireless sensor networks, pattern recognition, document classification, rideshare data analysis and diagnostic systems (Jafar, 2018). The advantages of k-means clustering are that it is relatively simple to perform, assemble stable and tight clusters, converges after some interactions and low computational cost (Chauhan & Gupta, 2018).…”
Section: Introductionmentioning
confidence: 99%