نتایج جستجو برای: subtractive fuzzy c means

تعداد نتایج: 1453543  

2009
Thomas A. Runkler

Herding is the process of bringing individuals (e.g. animals) together into a group. More specifically, we consider self– organized herding as the process of moving a set of individuals to a given number of locations (cluster centers) without any external control. We formally describe the relation between herding and clustering and show that any clustering model can be used to control herding p...

Journal: :International Journal of Computer Applications 2012

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

2013
Ramjeet Singh Yadav P. Ahmed

In this paper, we explore the applicability of Subtractive Clustering Technique (SCT) to student allocation problem that allocates new students to homogenous groups of specified maximum capacity, and analyze effects of such allocations on the academic performance of students. The paper also presents a Fuzzy set, Subtractive Clustering Technique (SCT) and regression analysis based Subtractive Cl...

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

Journal: :Appl. Soft Comput. 2008
M. Eftekhari S. D. Katebi M. Karimi A. H. Jahanmiri

In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally, cen...

Journal: :iranian journal of fuzzy systems 2008
e. mehdizadeh s. sadi-nezhad r. tavakkoli-moghaddam

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

2016
Dilip Singh Sisodia Shrish Verma Om Prakash Vyas

Clustering of web user sessions is extremely significant to comprehend their surfing activities on the internet. Users with similar browsing behaviour are grouped together, and further analysis of discovered user groups by domain experts may generate usable and actionable knowledge. In this paper, a conglomerative clustering approach is presented to identify web user session clusters from web s...

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