A Review: Comparative Study of Various Clustering Techniques in Data Mining

Clustering is a data mining technique of grouping set of data objects into multiple groups or clusters so that objects within the cluster have high similarity, but are very dissimilar to objects in the other clusters. Dissimilarities and similarities are assessed based on the attribute values describing the objects. Clustering algorithms are used to organize data, categorize data, for data compression and model construction, for detection of outliers, etc. Common approach for all clustering techniques is to find clusters centre that will represent each cluster. Cluster centre will represent with input vector can tell which cluster this vector belong to by measuring a similarity metric between input vector and all cluster centre and determining which cluster is nearest or most similar one.

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Topic: Data Management Date Added: Mar 2013 Format: PDF

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