Volume 17, No. 2, 2020
Development of a Unifying Theory for Data Mining Using Clustering Techniques
Yaser Issam Hamodi, Ruaa Riyadh Hussein and Naeem Th. Yousir
Abstract
A performance evaluation of four different clustering techniques was carried out based on segmenting consumer by product type and by product usage in the research. Cobweb, DBSCAN, EM and k-means algorithms were evaluated based on the computational time, accuracy of the result produced and the purity of the result produced. The experiment was performed using WEKA as a data mining tool. The performance evaluation of the four techniques showed that K-means outperformed others in all considered evaluation measure while the EM technique was the second best in terms of accuracy and purity, outperforming the other two. DBSCAN technique was the 3rd best of the selected algorithms even as its computational time is shorter than that of EM while the fourth best performing calculation has been believed to be the Spider web calculation as respects to immaculateness, exactness and computational time.
Pages: 01-14
DOI: 10.14704/WEB/V17I2/WEB17012
Keywords: Cobweb, DBSCAN, EM, K-means Algorithm, WEKA.