Telecommunications User Behaviors Analysis Based on Fuzzy C-Means Clustering

Guo, Zhe; Fu-rong, Wang

doi:10.1007/978-3-642-17569-5_57

Cited by 4 publications

(1 citation statement)

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“…Li Xiang Jun et al [10] proposed a solution of cluster centers and attached matrix into application of Fuzzy C-means clustering algorithm in macro-economic forecast. Zhe Guo and Furong Wang [11] modeled telecommunication user behavior based on the incoming/outgoing call holding time and then use fuzzy c-means algorithm to classify every level in user pyramidal model. Zhiye Sun et al [12] studied fuzzy c-means algorithm and applied in meteorological data.…”

Section: Review Of Literaturementioning

confidence: 99%

A New Hybrid Hard-Fuzzy (K-Mfcm) Data Clustering Method for Finding Cluster Centroid

Jafar¹

2018

ijarcs

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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 efficient in handling outlier data points. In this paper, a new hybrid algorithm is proposed to solve the limitations of the traditional clustering methods. The hybrid K-MFCM algorithm is tested on four real world bench mark data sets from UCI machine learning repository with various distance metrics including Euclidean, City Block and Chessboard. The cluster centroid values of hybrid algorithm are calculated for various data sets. The experimental results show that the hybrid algorithm gives good results in terms of objective function value and better fuzzy cluster validity results for chessboard distance metric than other distance metrics.

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Section: Review Of Literaturementioning

confidence: 99%