Comparing EM Clustering Algorithm with Density Based Clustering Algorithm Using WEKA Tool

Dr. Abdelrahman Elsharif Karrar, Moez Mutasim

Abstract: Machine learning is type of artificial intelligence wherein computers make predictions based on data. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with two clustering algorithms which are EM and Density based algorithm. EM algorithm is general method of finding the maximum likelihood estimate of data distribution when data is partially missing or hidden. In Density based clustering, clusters are dense regions in the data space, separated by regions of lower object density. The comparison between the above two algorithms is carried out using open source tool called WEKA, with the Weather dataset as its input.

Keywords: Machine learning, Unsupervised learning, supervised learning, EM clustering, Density based clustering, WEKA

How to Cite?: Dr. Abdelrahman Elsharif Karrar, Moez Mutasim, "Comparing EM Clustering Algorithm with Density Based Clustering Algorithm Using WEKA Tool", Volume 5 Issue 7, July 2016, International Journal of Science and Research (IJSR), Pages: 1199-1201, https://www.ijsr.net/getabstract.php?paperid=ART2016420, DOI: https://dx.doi.org/10.21275/ART2016420