fuzzy c-cluster analysis
Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
... Cluster tendency analysis can be done by visually inspecting the reordered distance matrix of the given dataset known as the visual assessment of cluster tendency (VAT) [23] ...
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Fuzzy equivalence relation based clustering and its use to restructuring websites' hyperlinks and web pages
... including cluster analy- sis, factor analysis, discriminant analysis [16], k-means analysis [14], c-means clus- tering ...the fuzzy c-means. Cluster ...
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A new hybrid of fuzzy c means method and fuzzy linear regression model in predicting manufacturing income
... Thus, fuzzy structure system is ...suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression ...
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An Efficient Approach to Enhance Performance of FCM Using PFA
... comparative analysis between k-means cluster algorithmic rule and fuzzy cluster algorithmic ...of fuzzy c- means algorithmic rule’s-means may be a partional primarily {based} ...
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A Configurable Routing Protocol for Improving Lifetime and Coverage Area in Wireless Sensor Networks
... in cluster analysis, especially in pattern ...distance. Fuzzy C-Means is very similar to K-Means in respect to the formation of clusters, but instead of assigning each node to a single ...
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Research on Fuzzy C Means Algorithm Based on the Information Entropy
... clustering analysis is one of the hard division and each sample will be strictly belong to one class with well-defined ...The fuzzy set theory proposed by Zadeh provides an important theoretical basis ...of ...
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Comparative Study of Fuzzy k Nearest Neighbor and Fuzzy C means Algorithms
... in-depth analysis of the clustering steps and visualization of the best clusters, ...of cluster fuzziness (the ‘m’ parameter) has been examined thoroughly on the clustering performance in terms of ...
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A Review of Image Segmentation of Underwater Images Using Fuzzy C- Means Clustering
... Fuzzy clustering based methods are basically used for medical imaging ...are Fuzzy C-Means clustering and K-Means ...using Fuzzy C- Means clustering for image segmentation. ...
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FCM-LB: Fuzzy C Means Cluster Based Load Balancing in Cloud
... a Cluster based load balancing algorithm which works well in heterogeneous nodes environment, considers resource specific demands of the tasks and reduces scanning overhead by dividing the Virtual machines into ...
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Enhancing the Performance of Web Proxy Server and Cluster the Data using Fuzzy C-Means Algorithm
... Clustering analysis finds clusters of data objects that are similar in some sense to one ...a cluster are more like each other than they are like members of other ...clustering analysis is to find ...
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Classification of EU Countries in the Context of Corporate Income Tax
... Parallel Analysis, Optimal Coordinate and Acceleration Factor (Costello and Osborne, ...the cluster analysis method using both a traditional hierarchical cluster analysis with Ward’s ...
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AN ITERATIVE GENETIC ALGORITHM BASED SOURCE CODE PLAGIARISM DETECTION APPROACH USING NCRR SIMILARITY MEASURE
... 725 CVI considers two parameters in its execution, namely compactness and separation of the data contained in an underlying data set [13, 14]. Compactness is the degree of closeness among the members of a cluster. ...
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Landslide Monitoring Point Optimization Deployment Based on Fuzzy Cluster Analysis
... In recent years, research on landslide monitoring has been paid highly attention by researchers both at home and abroad, and significant progress has been made. At present, many landslides have carried out a wide range ...
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Information fusion of ITS based on granular computing
... decision analysis, process control, pattern recognition and data ...system analysis of the cause of the incident, and traffic flow characteristics ...data analysis and data processing, mainly ...
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Semi Hausdorff Fuzzy Filters
... a fuzzy filter. A subset S of X is sequentially fuzzy filterly compact if every sequence in S has subsequence converging fuzzy filterly to a point in ...a fuzzy filter. A subset S of X is ...
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A Fuzzy Multicriteria Grade Cluster Analysis for the Budget Allocation of Tourism and Leisure Resources
... Cost-Benefit Analysis (CBA) or Single-objec- tive Mathematical Programming ...a fuzzy environment [1], while Baas and Kwakernaak (1997) proposed to use preferred fuzzy sets as the method of ranking ...
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Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
... by fuzzy c-means, fuzzy robust c-means, and fuzzy kernel robust ...the fuzzy kernel robust c-means is the best among the other three ...
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Dominant Problems of Rural Deprived Persons with Disability Using Fuzzy Cluster Analysis
... The Fuzzy Clustering analysis highlighted “Poverty” (11) as the main problem, solving which the PWDs could be relieved of their stressful marginalized ...
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Fuzzy clustering with volume prototypes and adaptive cluster merging
... The threshold above which merging takes place depends on the characteristics of the data set (separation between groups, cluster density, cluster size, etc.) and the clustering parameters such as the ...
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Performance Comparison of Hard and Soft Approaches for Document Clustering
... clustering techniques: “Partitioning” and “Hierarchical” [9]. Most document clustering algorithms can be classified into these two groups. Hierarchical techniques produce a nested sequence of partition, with a single, ...
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