**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.

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