scispace - formally typeset
Book ChapterDOI

A New Rough-Fuzzy Clustering Algorithm and its Applications

01 Jan 2014-pp 1245-1251
TL;DR: A robust rough-fuzzy clustering algorithm is applied here to identify clusters having similar objects and it can find overlapping and vaguely defined clusters with arbitrary shapes in noisy environment.
Abstract: Cluster analysis is a technique that divides a given data set into a set of clusters in such a way that two objects from the same cluster are as similar as possible and the objects from different clusters are as dissimilar as possible. A robust rough-fuzzy \(c\)-means clustering algorithm is applied here to identify clusters having similar objects. Each cluster of the robust rough-fuzzy clustering algorithm is represented by a set of three parameters, namely, cluster prototype, a possibilistic fuzzy lower approximation, and a probabilistic fuzzy boundary. The possibilistic lower approximation helps in discovering clusters of various shapes. The cluster prototype depends on the weighting average of the possibilistic lower approximation and probabilistic boundary. The reported algorithm is robust in the sense that it can find overlapping and vaguely defined clusters with arbitrary shapes in noisy environment. The effectiveness of the clustering algorithm, along with a comparison with other clustering algorithms, is demonstrated on synthetic as well as coding and non-coding RNA expression data sets using some cluster validity indices.
Citations
More filters

01 Jan 2014-
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
Abstract: Data mining techniques have been used widely in many areas such as business, science, engineering and medicine The techniques allow a vast amount of data to be explored in order to extract useful information from the data One of the foci in the health area is finding interesting biomarkers from biomedical data Mass throughput data generated from microarrays and mass spectrometry from biological samples are high dimensional and is small in sample size Examples include DNA microarray datasets with up to 500,000 genes and mass spectrometry data with 300,000 m/z values While the availability of such datasets can aid in the development of techniques/drugs to improve diagnosis and treatment of diseases, a major challenge involves its analysis to extract useful and meaningful information The aims of this project are: 1) to investigate and develop feature selection algorithms that incorporate various evolutionary strategies, 2) using the developed algorithms to find the “most relevant” biomarkers contained in biological datasets and 3) and evaluate the goodness of extracted feature subsets for relevance (examined in terms of existing biomedical domain knowledge and from classification accuracy obtained using different classifiers) The project aims to generate good predictive models for classifying diseased samples from control

4 citations


Journal Article
TL;DR: This paper proposes Fuzzy to Rough FuzzY Link Element (FRFLE) which is used as an important factor to conceptualize the rough fuzzy clustering from the fuzzy clusters result and shows that proposed RFCM algorithm using FRFLE deals with less computation time than the traditional RFCM algorithms.
Abstract: Clustering is a standard approach in analysis of data and construction of separated similar groups. The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering. The prominent feature of soft clustering leads to combine the rough and fuzzy sets. The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuzzy sets into c-means algorithm, the widespread RFCM needs more computation. To avoid this, this paper proposes Fuzzy to Rough Fuzzy Link Element (FRFLE) which is used as an important factor to conceptualize the rough fuzzy clustering from the fuzzy clustering result. Experiments with synthetic, standard and the different benchmark dataset shows the automation process of the FRFLE value, then the comparison between the results of general RFCM and RFCM using FRFLE is observed. Moreover, the performance analysis result shows that proposed RFCM algorithm using FRFLE deals with less computation time than the traditional RFCM algorithms.

2 citations


Cites methods from "A New Rough-Fuzzy Clustering Algori..."

  • ...RFCM algorithm has been widely used inmany applications [10,12,13,5]....

    [...]


Book ChapterDOI
22 Oct 2021-
Abstract: Rough k-means algorithm can effectively deal with the problem of the fuzzy boundaries. But traditional rough k-means algorithm set unified weight for boundary object, ignoring the differences between individual objects. Membership degree method of rough fuzzy k-means algorithm is used to measure the membership degree of boundary object to the clusters that they may belong to, ignoring the distribution of neighbor points of the boundary object. So, according to the distribution of neighbor points of the boundary object, we put forward a new rough k-means algorithm to measure the weight of boundary objects. The proposed algorithm considers the distance from boundary objects to their neighbor points and the number of neighbor points of boundary objects together to dynamically calculate the weights of boundary object to clusters that may belong to. Simulation and experiment, through examples verify the effectiveness of the proposed method.

References
More filters

01 Jan 1967-
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special

22,533 citations


Book
31 Jul 1981-
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

15,070 citations


Journal ArticleDOI
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

13,346 citations


01 Jan 1988-

9,245 citations


Book
01 Jan 1988-

8,580 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20211
20171
20141