Introduction to Seminar Topic on Data Clustering and Applications:
Introduction to data clustering:
Due to storage of large databases the fast retrieval of information has become a growing need. To achieve fast retrieval of information various methods are being used but data clustering has is efficient method for this purpose. To improve the processing speed the information is stored which is of logically similar and they are physically stored together. In this paper we will how data clustering method is implemented and its applications in the fields of data mining.
Data clustering method and implementation:
In data clustering process the entire databases are divided into clusters on a similarity of characteristics. To avoid the confusion regarding the classification of the clusters the classifications are provided to the predefined classes. The similar clusters are taken in regular intervals and the different clusters are determined by calculating the distance the intervals. A similarity matrix form is used for calculating the similarity of the objects.
There are different clustering methods available depending upon the output requirement the particular methods is implemented. One of the important clustering methods are portioning method in which the clusters are represented in the form of centroid and the other method is hierarchical algorithm method in which closely related clusters are merged together.
Applications of data clustering:
The data clustering has immense number of applications in the life of the mankind. Data clustering has various applications related to database in the computer fields. These are mainly used in the information retrieval systems.
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