A Survey on Partitioning and Hierarchical based Data Mining Clustering Techniques

摘要

In Data Mining, Clustering is a general technique for statistical data analysis, which is used in dissimilar fields, including machine learning, pattern recognition, image analysis and bioinformatics. Clustering is an excellent data mining tool for a huge and multivariate database. It is the one of data mining techniques in which data is separated into the set of related objects. Clustering is an appropriate example of unsupervised classification. It means that clustering does not depend on pre-defined classes and training examples through classifying the data objects. A Partitioning and Hierarchical algorithm in data mining is the most active research algorithm among proposed algorithms. Several factors or themes determine the optimal actual clustering. The significant idea of this paper is classifying the methods on the bases of different themes so that it aids in choosing algorithms for some further improvement and optimization. In this survey paper, a review of clustering, partitioning and hierarchical based clustering techniques and evaluation metrics for clustering are discussed.