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Proceedings ArticleDOI

Correlative analysis of soft clustering algorithms

01 Dec 2013-pp 360-365
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
Citations
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Proceedings ArticleDOI
01 Jun 2018
TL;DR: A novel method is proposed to achieve a high recognition rate in many real-life surveillance zones such as banks, airports and corridors, based on dividing a gait cycle into several phases using Constrained Fuzzy C-Means method and converging feature information of a stream into one feature descriptor using gait Cycle analysis.
Abstract: Gait as a significant biometric feature in human identification is drawing a wide attention nowadays. In many real-life surveillance zones such as banks, airports and corridors, gait recognition is often restricted from the front view. There are situations where a complete gait cycle is not always available due to frame drop caused by devices and the limitation in space of such areas, while most of the existing methods require at least one complete gait cycle. A novel method is proposed to achieve a high recognition rate in such application scenarios, based on dividing a gait cycle into several phases using Constrained Fuzzy C-Means method and converging feature information of a stream into one feature descriptor using gait cycle analysis. Experimental results demonstrate the high performance of our method comparing to other existing ones.

4 citations


Cites methods from "Correlative analysis of soft cluste..."

  • ...is the same with the one of Fuzzy C-Means [25]....

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  • ...It is obvious that more feature information belonging to the kth gait phase is captured, more similar the probe gait represented by Q̂probe will be to the kth average gait which is modeled by the corresponding cluster centroid Zk obtained in Constrained Fuzzy C-Means....

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  • ...Constrained Fuzzy C-Means is proposed to classify each gallery frame represented by a vector named EigenSilhouette into two neighboring clusters (gait phases), with membership values corresponding to the rest of the clusters equal to 0....

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  • ...(3) Then the procedure of minimizing the objective function J = M∑ m=1 Nm∑ n=1 K∑ k=1 (wm,nk ) q D (Fmn ,Zk), (4) is the same with the one of Fuzzy C-Means [25]....

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  • ...Constrained Fuzzy C-Means as a gait phase classification method for gallery streams is described in Section II-A, along with gait phase descriptors, followed by feature descriptor and gait cycle analysis of a probe stream in Section II-B....

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DissertationDOI
01 Jan 2015

3 citations


Cites background from "Correlative analysis of soft cluste..."

  • ...(soft) clustering approaches can be found in [50]....

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References
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Journal ArticleDOI
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

5,744 citations

Journal ArticleDOI
TL;DR: The fundamental concepts of cluster validity are introduced, and a review of fuzzy cluster validity indices available in the literature are presented, and extensive comparisons of the mentioned indices are conducted in conjunction with the Fuzzy C-Means clustering algorithm.

489 citations


"Correlative analysis of soft cluste..." refers background in this paper

  • ...RCM, [3] a newly emerged algorithm by the theory of rough sets to handle uncertainties more precisely based on upper and lower approximations....

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Journal ArticleDOI
01 Aug 2006
TL;DR: A novel clustering architecture is introduced, in which several subsets of patterns can be processed together with an objective of finding a common structure, and the required communication links are established at the level of cluster prototypes and partition matrices.
Abstract: In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data

241 citations

Journal ArticleDOI
TL;DR: A rough-fuzzy hybridization scheme for case generation that makes the algorithm suitable for mining data sets, large both in dimension and size, due to its low-time requirement in case generation as well as retrieval.
Abstract: We propose a rough-fuzzy hybridization scheme for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space. The fuzzy membership functions corresponding to the informative regions are stored as cases along with the strength values. Case retrieval is made using a similarity measure based on these membership functions. Unlike the existing case selection methods, the cases here are cluster granules and not sample points. Also, each case involves a reduced number of relevant features. These makes the algorithm suitable for mining data sets, large both in dimension and size, due to its low-time requirement in case generation as well as retrieval. Superiority of the algorithm in terms of classification accuracy and case generation and retrieval times is demonstrated on some real-life data sets.

200 citations

Journal ArticleDOI
TL;DR: A refined rough cluster algorithm is presented that is applied to synthetic, forest and microarray gene expression data and successfully applied to web mining.

191 citations