Cluster analysis is defined as the process of classifying objects of similar nature into groups called clusters. The objects in the similar cluster will share a strong bond with each other but their association with objects in other cluster will be weak. Cluster analysis is done using algorithms. The division of this algorithms and the population density of objects in each cluster is done using trial and error basis. The parameters are tweaked and tested until a correct result is found. The objects are distributed statistically in each cluster; results are found using data mining technique. Some groups may classify automatically but some has to be classified accordingly. We can distinguish an algorithm by its cluster. Some of the typical cluster models are connectivity, centric, distribution, density, subspace, group etc. The inter cluster relationship may be specified in cluster analysis in which clustering is roughly distinguished as hard and soft depending on the belonging object and which can be further divided into strict partitioning, hierarchical clustering etc.
Cluster validation is an important step in cluster analysis; it is the evaluation of similarity between two clusters. Cluster analysis are used everywhere be it during public census, classification of species of animals and plants or similar diagnostic cases . To run a good business, classification and clustering your work is as important as taking other managerial decisions
The main areas of usage of cluster analysis
- To analyze new thesis on origin of species
- For studying the market trends topology wise
- Forecasting the consumer behavior
- Optimization in production design
- The cure and treatment plan for various diseases
- Various astronomical theories and proposals
- Formulation of economic growth
- Dividing and classifying human genes
- Analyzing an area’s crimes and investigation
Though the cluster analysis may not give hundred percent accurate results, it would help in bringing down discrimination and chaos in many areas.
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