A Review: Design and Development of Novel Techniques for Clustering and Classification of Data

Abstract

Data Distribution can be obtained by clustering data. In this work we observed the characteristics of selected cluster, and make a further study on particular clusters. Also, cluster analysis generally acts as the preprocessing of other data mining operations. Consequently, cluster analysis has become a very active research topic in data mining. Data mining is a new technology, developing with database as well as artificial intelligence. It is a processing procedure of extracting credible and effective novel techniques and understandable patterns from the database. Cluster analysis can be important data mining method used to figure out the data segmentation and pattern information. The development of data mining methods, different types of clustering techniques establish. The study of clustering method from the perception of statistics, based on the statistical theory, The review of this paper make an effort to combine statistical method with the machine learning algorithm technique as well as introduce the existing best r-statistical softwares, including factor, correspondence and analysis of functional data into data mining. The present study is undertaken to develop a Data Mining workflow using clustering and classification of data, solving clustering problem as well as extracting association rules. Use the suitable proximity measure in addition to that to select the optimal clustering model to solve clustering problems. Develop a Data Mining workflow to extract association rules.