A Rough-Fuzzy approach for Support Vector Clustering

doi:10.1016/J.INS.2015.12.035

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A Rough-Fuzzy approach for Support Vector Clustering

Journal Article•DOI•

A Rough-Fuzzy approach for Support Vector Clustering

Ramiro Saltos¹, Richard Weber¹•Institutions (1)

University of Chile¹

20 Apr 2016-Information Sciences (Elsevier)-Vol. 339, Iss: 339, pp 353-368

TL;DR: A novel extension of this clustering algorithm, called Rough-Fuzzy Support Vector Clustering (RFSVC), that obtains rough-fuzzy clusters using the support vectors as cluster representatives, showing its potential for detecting outliers and computing membership degrees for clusters with any silhouette.

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About: This article is published in Information Sciences.The article was published on 2016-04-20. It has received 34 citations till now. The article focuses on the topics: Fuzzy clustering & Correlation clustering.

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Journal Article•DOI•

Shopping with a robotic companion

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Francesca Bertacchini¹, Eleonora Bilotta, Pietro Pantano•Institutions (1)

University of Calabria¹

01 Dec 2017-Computers in Human Behavior

TL;DR: A robotic shopping assistant, designed with a cognitive architecture, grounded in machine learning systems, is presented in order to study how the human-robot interaction (HRI) is changing the shopping behavior in smart technological stores.

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121 citations

Journal Article•DOI•

An approach to fault diagnosis with online detection of novel faults using fuzzy clustering tools

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Adrián Rodríguez-Ramos¹, Antônio José da Silva Neto², Orestes Llanes-Santiago¹•Institutions (2)

Polytechnic José Antonio Echeverría¹, Rio de Janeiro State University²

15 Dec 2018-Expert Systems With Applications

TL;DR: This paper presents an approach to fault diagnosis with online detection of novel faults and automatic learning using fuzzy clustering techniques, and the results obtained indicate the feasibility of the proposed method.

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Abstract: This paper presents an approach to fault diagnosis with online detection of novel faults and automatic learning using fuzzy clustering techniques. In the off-line learning stage, the classifier is trained to diagnose the known faults and the normal operation state using the Density Oriented Fuzzy C-Means and the Kernel Fuzzy C-Means algorithms. In this stage, the historical data previously selected by experts, are firstly pre-processed to eliminate outliers and reduce the confusion in the classification process by using the Density Oriented Fuzzy C-Means algorithm. Later on, the Kernel Fuzzy C-Means algorithm is used for achieving greater separability among the classes and reducing the classification errors. Finally, the optimization of the two parameters used by these algorithms in the training stage is developed by using a bio-inspired optimization algorithm, namely the differential evolution. After the training, the classifier is used online (online diagnosis stage) in order to classify the new observations that are collected from the process. In this stage, the detection of novel faults based on density by using the DOFCM algorithm is applied. The algorithm analyzes the observations belonging to a window of time which were not classified into the known classes and it is determined if they are a new class or outliers. If a new class is identified, a procedure is developed to incorporate it to the known classes set. The proposed approach was validated using the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) benchmark. The results obtained indicate the feasibility of the proposed method.

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41 citations

Journal Article•DOI•

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

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Abbas Mardani, Mehrbakhsh Nilashi, Jurgita Antucheviciene, Madjid Tavana, Romualdas Bausys, Othman Ibrahim - Show less +2 more

29 Oct 2017-Complexity

TL;DR: The attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory and the Web of Science database has been chosen to select the relevant papers.

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Abstract: Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called “PRISMA,” has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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30 citations

Cites methods from "A Rough-Fuzzy approach for Support ..."

...Apart from using fuzzy-rough set theory for preprocessing, it has also been used successfully to tackle classification problems directly, for example, in rule induction [157, 170, 286, 287], decisionmaking [49, 59, 65, 288] improving 𝐾-NN classification [87, 289–293], interval-valued fuzzy sets [108, 122, 130, 133, 294, 295], enhancing decision trees [165, 296, 297], hesitant fuzzy sets [132, 133, 137, 173, 298], and boosting SVMs [11, 98] [25, 26, 68, 106]....
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...For example, Support Vector Machines (SVMs) [98, 271–273] construct a function that models a separating border between the different classes in the data, and the value of that function for the new instance then determines to what class it most likely belongs....
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...Apart from using fuzzy-rough set theory for preprocessing, it has also been used successfully to tackle classification problems directly, for example, in rule induction [157, 170, 286, 287], decisionmaking [49, 59, 65, 288] improving K-NN classification [87, 289–293], interval-valued fuzzy sets [108, 122, 130, 133, 294, 295], enhancing decision trees [165, 296, 297], hesitant fuzzy sets [132, 133, 137, 173, 298], and boosting SVMs [11, 98] [25, 26, 68, 106]....
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Journal Article•DOI•

An eigenvector based center selection for fast training scheme of RBFNN

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Yanxing Hu¹, Jane Jia You¹, James N. K. Liu¹, Tiantian He¹•Institutions (1)

Hong Kong Polytechnic University¹

01 Feb 2018-Information Sciences

TL;DR: An eigenvector based clustering method is employed to calculate the RBF centers in the input feature space and it shows that the proposed method greatly reduces the training time of an RBFNN while allowing theRBFNN to attain a comparable accuracy result.

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21 citations

Journal Article•DOI•

A novel fault diagnosis scheme applying fuzzy clustering algorithms

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A. Rodríguez Ramos¹, Orestes Llanes-Santiago¹, J.M. Bernal de Lázaro¹, C. Cruz Corona², A.J. Silva Neto³, J.L. Verdegay Galdeano² - Show less +2 more•Institutions (3)

Polytechnic José Antonio Echeverría¹, University of Granada², Rio de Janeiro State University³

01 Sep 2017-Applied Soft Computing

TL;DR: As a first part of the classification process, the data was pre-processed to eliminate outliers and reduce the confusion and the obtained results indicate the feasibility of the proposal.

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20 citations

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References

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Book•

Fuzzy sets

[...]

Lotfi A. Zadeh

01 Aug 1996

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

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Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

...read moreread less

52,705 citations

UCI Machine Learning Repository

[...]

A. Asuncion

01 Jan 2007

17,341 citations

Book•

Pattern Recognition with Fuzzy Objective Function Algorithms

[...]

James C. Bezdek

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.

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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.

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15,662 citations

"A Rough-Fuzzy approach for Support ..." refers background in this paper

...In García et al. [11], subtractive clustering [7] was used to obtain the class center of each cluster; then support vector machines (SVM) for density estimation were used; support vectors were found; and the membership degrees for the elements in the clusters were calculated based on the idea of Fuzzy C-Means, i.e. in an iterative fashion....
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...One advantage of this matrix over the matrix provided by Fuzzy C-Means is that the sum of the membership degrees in each row of the former is not necessarily 1....
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...However, their use is still limited by some characteristics, such as clusters with spherical shapes, the fact that the sum of the membership values of an object has to be equal to 1 (Fuzzy C-Means), the need to know the number of clusters beforehand, and that the data points identified as outliers are not classified accordingly....
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...We set the parameters for Rough–Fuzzy Support Vector Clustering following Ben-Hur’s suggestions [4], and similarly, for Rough–Fuzzy C-Means and Rough–Possibilistic C-Means, we used the ideas reported by Maji and Pal [23]....
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...Then, in Section 4.2, we present the results obtained using Rough–Fuzzy Support Vector Clustering, Rough– Fuzzy C-Means, and Rough–Possibilistic C-Means....
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Journal Article•DOI•

Survey of clustering algorithms

[...]

Rui Xu¹, Donald C. Wunsch¹•Institutions (1)

Missouri University of Science and Technology¹

01 May 2005-IEEE Transactions on Neural Networks

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.

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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.

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5,744 citations

"A Rough-Fuzzy approach for Support ..." refers background in this paper

...Many clustering algorithms have been proposed in the literature [10,14,27,34,36,37], which can be grouped into two categories: hard clustering, and soft clustering....
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Journal Article•DOI•

On Clustering Validation Techniques

[...]

Maria Halkidi¹, Yannis Batistakis¹, Michalis Vazirgiannis¹•Institutions (1)

Athens University of Economics and Business¹

02 Dec 2001

TL;DR: The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.

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Abstract: Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in large data sets. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Especially, in the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. This paper introduces the fundamental concepts of clustering while it surveys the widely known clustering algorithms in a comparative way. Moreover, it addresses an important issue of clustering process regarding the quality assessment of the clustering results. This is also related to the inherent features of the data set under concern. A review of clustering validity measures and approaches available in the literature is presented. Furthermore, the paper illustrates the issues that are under-addressed by the recent algorithms and gives the trends in clustering process.

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2,643 citations