DATA CLUSTERING USING CLUSTER PATTERN ANALYSIS

Abstract

In today's world, we need to analyse and extract information from the data. The grouping is one of those analyses Method that consists in the distribution of data in groups of identical objects each group is known as a group, which consists of objects that have affinity within the cluster disparity with objects in other groups. This paper is intended examine and evaluate different data grouping algorithms. The Two main categories of cluster approaches are partition and hierarchical grouping. The algorithms discussed here are: k-means clustering algorithm, hierarchical clustering algorithm, density-based clustering algorithm, self-organized map Algorithm and grouping algorithm for maximizing expectations. All the mentioned algorithms are explained and analysed based on in factors such as the size of the data set, the type of data set, Number of created clusters, quality, accuracy and performance. This paper also provides information on the tools that they are used to implement cluster approaches