Comparative Analysis of K-Means and Fuzzy C- Means Algorithms

By Soumi Ghosh, Sanjay Kumar Dubey. 2013

544 citations recorded by the source corpus.

In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering. Keywords—clustering; k-means; fuzzy c-means; time complexity

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/714fa50674458f07ee9eeea9d2182a670a0bd88e

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Citations in the corpus

Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “The data points are then assigned to a cluster that has maximum posterior probability or can be analysed as different probabilistic distributions rather than a fixed assignment (Boydell and McBratney, 2002; Ghosh and Dubey, 2013).”
A review of clustering techniques and developments (2017, methodology): “Procedure for FCM FCM suffers from initial partition dependence, as well as noise and outliers like k-means.”