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cluster analysis and clustering techniques
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Cluster Analysis

Clustering can be considered the most important unsupervised learning problem as clustering deals with unlabeled data. So, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects which are similar between them and are dissimilar to the objects belonging to other clusters. If the similarity criterion is distance: the two or more objects belong to the same cluster if they are close according to
a given distance (geographical distance in case of wireless sensors networks).
 

Clustering Technique

There are several ways to cluster a data set. Moreover, all methods are capable of performing their task for multivariable data set. While handling such a problem, every method uses its algorithm. Following are different algorithms which are also the names of the method.

Heuristic-based

o Prototype based (k-means, k-medoid)

o Linkage Methods (single link, complete link)

o Graph Theoretic (MST, spectral clustering)

Model based

o Spatial Clustering

o Mixture Model (Gaussian Mixture)

Density Based

o Mode seeking (means shift)

o Kernel Based (DENCLUE)

o Grid Based (wave-Cluster, STING)

All the listed algorithms are different approaches for an unsupervisedprocedure. Moreover, every algorithm has a criterion function which they have to optimize.