Authors : Himanika , Vishesh Mehta and Poonam Nandal
Abstract: Extraction of information from a database is a major issue these days. There is huge amount of information available in web in the form of web pages which is used to extract as per the need of the user to perform a vital task. To overcome this issue of information retrieval various techniques are known today like clustering, classification, natural language processing techniques etc. In this study, we have discussed various clustering methods algorithms with various features to classify the data. k-means clustering algorithm is majorly used to cluster the data which is also focussed in this study. The capability of k-means clustering algorithm is due to its computational competence. k-means is a clustering technique in which similar data points are grouped into clusters. In this study, we have proposed a clustering algorithm based on the density of data points and used Manhattan distance for grouping the data points into a cluster. It has been empirically found that the results of proposed clustering algorithm provide better clusters as compared to existing clustering algorithms.
Himanika , Vishesh Mehta and Poonam Nandal, 2017. Efficient Data Clustering Algorithm Designed for 2-D Dataset. Journal of Engineering and Applied Sciences, 12: 5485-5489.