A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
Authors
Abstract:
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data. The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor. Comparisons and analysis of the experimental results demonstrate the capability of the proposed method.
similar resources
Fuzzy c-means clustering of incomplete data
The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values. For example, a particular datum x(k) might be incomplete, having the form x(k)=(25...
full textOPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM
This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...
full textFuzzy c-means clustering algorithm for directional data (FCM4DD)
Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions...
full textA Fuzzy Clustering Model of Data and Fuzzy c-Means
The Multiple Prototype Fuzzy Clustering Model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identi...ed. In the model, it is assumed that the membership of each entity to a cluster expresses a part of the cluster prototype re‡ected in the e...
full textoptimization of fuzzy clustering criteria by a hybrid pso and fuzzy c-means clustering algorithm
this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...
full textA Fuzzy C-means Clustering Algorithm Based on Pseudo-nearest-neighbor Intervals for Incomplete Data ⋆
Missing data handling is a challenging issue often dealt with in data mining and pattern classification. In this paper, a fuzzy c-means clustering algorithm based on pseudo-nearest-neighbor intervals for incomplete data is given. The data are first completed using the pseudo-nearest-neighbor intervals approach, then the data set can be clustered based on the fuzzy c-means algorithm for interval...
full textMy Resources
Journal title
volume 8 issue 4
pages 515- 523
publication date 2020-11-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023