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Proceedings ArticleDOI

Integrating rough clustering with Fuzzy sets

20 Jul 2011-pp 865-869
TL;DR: This paper presents the evolution and importance of clustering techniques, since clustering is unsupervised learning and there are many clustering methods in practice which results in which clustering scheme to be selected for this purpose.
Abstract: This paper presents the evolution and importance of clustering techniques, since clustering is unsupervised learning and there are many clustering methods in practice which results in which clustering scheme to be selected for our purpose .Here we take four clustering methodologies crisp Juzzy rough and rough fuzzy. These clustering methods have been implemented and its importance over one another is explained. And the suitable clustering method over these three has been identified for better perspective. The experiment results with the sample dataset illustrate the importance of clustering schemes.
Citations
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Journal ArticleDOI
TL;DR: An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper and the validity of this algorithm is demonstrated by simulation and experimental analysis.
Abstract: Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.

46 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.

3 citations

Journal ArticleDOI
TL;DR: An improved algorithm of rough k-means clustering based on variable weighted distance measure is presented and Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.
Abstract: Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cluster centers when computing new mean for each cluster. To solve this issue, an improved algorithm of rough k-means clustering based on variable weighted distance measure is presented in this article. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.

2 citations


Cites methods from "Integrating rough clustering with F..."

  • ...Many improvements to the rough k-means algorithm [4-14, 8-20] are emerging in the past ten years....

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Journal ArticleDOI
TL;DR: Decision theory is propagated, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks.
Abstract: Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining.

2 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: Calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis are reviewed.
Abstract: Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups Clustering can be considered one of the most important unsupervised learning techniques so as every other problem of this kind; it deals with finding a structure in a collection of unlabelled data Clustering is of soft and hard clustering Hard clustering refers to basic partitioning algorithms where object belongs to only one cluster Soft clustering refers to data objects belonging to more than one cluster based on its membership values This paper reviews three types of Soft clustering techniques: Fuzzy C-Mean, Rough C-Mean, and Rough Fuzzy C-Mean Thereby calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis

2 citations


Cites background from "Integrating rough clustering with F..."

  • ...In this paper, Fuzzy C-Mean (FCM), Rough C-Mean (RCM), and Rough Fuzzy C-Mean (RFCM) soft clustering algorithms are used....

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  • ...)} DB £.., " 1 " " C t= L * ) d( Ui , uj ) (9) 363 Algorithm Davies-Bouldin Index FCM 0.0646 RCM 0.0542 RFCM 0.0493 COMPARISON CHART 1: Chart for Davies-Bouldin Index on Sample data set Davies-Bouldi n Index 0.07 0.06 0.05 0.04 0.03 0.02 0 .01 COMPARISON CHART 3: Chart for Davies-Bouldin Index on Iris data set( c...

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  • ...RFCM provides minimum value for Davies-Bouldin index compared to Rough C-Mean and Fuzzy C-Mean....

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  • ...The concept of crisp lower bound and fuzzy boundary of a class introduced a new hybrid [2] unsupervised learning algorithm, coined as RFCM which enables efficient selection of cluster prototypes....

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  • ...0.32 0.315 0.31 0.305 0.3 0.295 Davies-Bouldi n Index 0.116 0.114 0.112 0.11 0.108 0.106 0.104 0.102 0.1 0.098 FCM RCM RFCM • Davies-Bouldi n...

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