A Study Of Data Mining Technique Using Constrained Based Clustering Methods

Clustering data mining is the process of putting together meaning-full or use-full similar object into one group. It is a common technique for statistical data, machine learning, and computer science analysis. Clustering is a kind of unsupervised data mining technique which describes general working behavior, pattern extraction and extracts useful information from electricity price time series. Clustering is a technique in which a given data set is divided into groups called clusters in such a manner that the data points that are similar lie together in one cluster. Clustering plays an important role in the field of data mining due to the large amount of data sets.This paper reviews the various clustering algorithms available for data mining and provides a comparative analysis of the various clustering algorithms. Clustering is defined as the unsupervised classification of the data items or the observations i.e. the data sets have not been classified into any group and so they do not have any class attribute associated with them. Clustering is widely used as one of the important steps in the exploratory data analysis.Clustering algorithms are used to find the useful and unidentified classes of patterns. Clustering is used to divide the data into groups of similar objects. The objects that are dissimilar are placed in separate clusters. Depending upon the metric chosen, a data object may belong to a single cluster or it may belong to more than one cluster. For example, consider a retail database that contains information about the items purchased by the consumers. Clustering will group the consumers according to their buying patterns. When we group the objects into clusters then simplification is achieved at the cost of losing some information.