A COMPARATIVE STUDY OF TWO SUBSPACE CLUSTERING ALGORITHMS: PREDECON AND CLIQUE

Abstract

The world we live in nowadays has the ability to analyze information in matter of seconds, which has made decision-making and resolving problems a must for fundamental success. It is vital for this process, an effective and flexible data analytics that serves to build precise versions swiftly and intuitively. Data analysis is a familiar technique used to analyze data in various fields of research. With the increase of modern (smart) devices, the amount of data is also increased which brings us to the increase of disciplines. This results in the development of various tools and algorithms for applying cluster analysis. A cluster is a set of similar entities that are grouped together. In fact none other thing than their similarity features they have and their approximation to each other define their place in the cluster. All similar data points form clusters or sets. Creating these clusters based on their resemblance of their qualities is called clustering. Every clustering algorithm has its advantages and of course its limitations. In most cases it varies from the complexity of available information. The current research is an attempt to analyze the data using clustering techniques. The research uses python language to compile a program to collect the data from a dataset that we feed. Python is used to analyze and clusters are used to interpret accordingly. The results of clustering data based on different dimensions will lead to improved knowledge about the data accordingly.