Fuzzy clustering

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Fuzzy clustering is a class of algorithm in computer science.

[edit] Explanation of clustering

Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Depending on the nature of the data and the purpose for which clustering is being used, different measures of similarity may be used to place items into classes, where the similarity measure controls how the clusters are formed. Some examples of measures that can be used as in clustering include distance, connectivity, and intensity.

In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels. These indicate the strength of the association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these membership levels, and then using them to assign data elements to one or more clusters.

[edit] See also

• evolving classification function
• neuro-fuzzy techniques
• FLAME Clustering

[edit] External links

• Extended Fuzzy Clustering Algorithms by Kaymak, U. and Setnes, M.
• Low-Complexity Fuzzy Relational Clustering Algorithms for Web Mining by Raghu Krishnapuram
• Wolfram Research
• A Fuzzy Clustering Algorithm by Erminio D. and Guerrisi F.
• Fuzzy Clustering Algorithms and their Application to Medical Image Analysis PhD Thesis, 2001, by AI Shihab.
• A Tutorial on Clustering Algorithms