Comparing EM Clustering Algorithm with Density Based Clustering Algorithm Using WEKA Tool
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Information Technology | Saudi Arabia | Volume 5 Issue 7, July 2016 | Popularity: 6.9 / 10


     

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


Edition: Volume 5 Issue 7, July 2016


Pages: 1199 - 1201


DOI: https://www.doi.org/10.21275/ART2016420



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

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