An Analysis of Clustering Techniques

Abstract

I analyzed different clustering methods of credit card customers data by some machine learning clustering algorithm i approached to classify the high dimensional data by using clustering algorithms in the real world ,It is a type of process where we divide the similar objects into one group The Objective of paper is to identify which method gives a general view of clustering techniques like K-means, algometrive and gaussian distribution ,to analyze Segmentation of customers can be used to define marketing strategies. Clustering comes under unsupervised learning, if we take data set it contains some input patterns, entities etc. The main aim of clustering is to make partition & to make clusters having similarity, so that we can get some useful insights and make further analyses .The process of finding closely connected and fair information from high dimensional datasets is complex and difficult to carry .In this paper we tried to analyze our model through different clustering models like K-means, DBSCAN, Agglomerative Hierarchical and gaussian mixture model) and I explained about all algorithm that I used unique approaches for clustering the high dimensional data.