نتایج جستجو برای: fuzzy clustering algorithm fca and nero
تعداد نتایج: 16943298 فیلتر نتایج به سال:
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...
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...
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...
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...
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.
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...
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...
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...
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...
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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