نتایج جستجو برای: fuzzy clustering algorithm fca and nero

تعداد نتایج: 16943298  

2004
George E. Tsekouras Dimitris Papageorgiou Sotiris B. Kotsiantis Christos Kalloniatis Panayiotis E. Pintelas

We develop a three-step fuzzy logic-based algorithm for clustering categorical attributes, and we apply it to analyze cultural data. In the first step the algorithm employs an entropy-based clustering scheme, which initializes the cluster centers. In the second step we apply the fuzzy c-modes algorithm to obtain a fuzzy partition of the data set, and the third step introduces a novel cluster va...

Journal: :JSW 2012
Linquan Xie Ying Wang Fei Yu Chen Xu Guangxue Yue

A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway; then, for the shortages of fuzzy Cmeans clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorith...

2005
Chih-Ching Hsiao Shun-Feng Su Chen-Chia Chuang

Traditional approaches for modeling TSK fuzzy rules are trying to adjust the parameters in models, and not considering the training data distribution. Hence it will result in an improper clustering structure, especially, when outliers exist. In this paper, a clustering algorithm termed as Robust Proper Structure Fuzzy Regression Algorithm (RPSFR) is proposed to define fuzzy subspaces in a fuzzy...

2007
MOH’D BELAL

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of dista...

2005
Vasile Patrascu

In this paper one presents a new fuzzy clustering algorithm based on a dissimilarity function determined by three parameters. This algorithm can be considered a generalization of the Gustafson-Kessel algorithm for fuzzy clustering.

Journal: :JIPS 2015
Prem Kumar Singh Cherukuri Aswani Kumar

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposi...

2008
KYOUNG-MO YANG

In order to analyze vague data set of uncertainty information, Fuzzy Formal Concept Analysis(FFCA) incorporates fuzzy set theory into Formal Concept Analysis(FCA). It extracts useful information with a unit of fuzzy concept from given fuzzy formal context with a confidence threshold. Then it constructs fuzzy lattice by order relations between the fuzzy concepts. In this paper, we introduce basi...

2014
Praveen Kumar Shukla Surya Prakash Tripathi

Fuzzy systems are capable to model the inherent uncertainties in real world problems and implement human decision making. In this paper two issues related to fuzzy systems development are addressed and solutions are proposed and implemented. First issue is related to the high dimensional data sets. Such kinds of data sets lead to explode the search space of generated rules and results into dete...

2017
Charu Puri Naveen Kumar

We propose a type-2 based clustering algorithm to capture data points and attributes relationship embedded in fuzzy subspaces. It is a modification of Gustafson Kessel clustering algorithm through deployment of type-2 fuzzy sets for high dimensional data. The experimental results have shown that type-2 projected GK algorithm perform considerably better than the comparative techniques. General T...

2002
WANG Shi-tong JIANG Hai-feng

In order to solve switching regression problems, many approaches have been investigated. In this paper, an integrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton’s Gravity Law. The theoretic analysis shows that GFC can converge to a ...

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