Clustering Analysis
Clustering analysis is finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters.
Two Categories of Clustering Analysis Methods:
1. Hierarchical Cluster Analysis Methods
a. Agglomerative Methods
In Agglomerative methods, all methods start in separate clusters till slowly similar objects are combined and this process is repeated till all objects are in a single cluster. Finally, the optimum number of clusters is chosen from among all options.
b. Divisive Methods
In Divisive Methods, all objects the same cluster and the reverse of the agglomerative method is used.
2. Non-hierarchical Cluster Analysis Methods
(known as k-means clustering methods)
K-means used when large data sets are involved. Further, these provide the flexibility of moving a subject from one cluster to another.
An example in daily life:
Clusters play an important role in "human development". For example, all natural objects are basically classified into groups: animals, plants, minerals. According to the biological taxonomy, all animals are grouped into categories of the kingdom, phylum, class, order, family, genus, species from the general to the specific. There are animals named tigers, lions, wolves, dogs, horses, sheep, cats, mice etc.
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