A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 11
Abstract
This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MC- DBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the pertinent criteria that allows the reclassification of objects of the two closest clusters through each iteration, as well as the aptitude of the auto-evaluation of the precision of the clustering during the clustering process. This work conducts the experiments using the well-known benchmark, ‘Seismic’, ‘Landform-Identification’ and ‘Image Segmentation’, to compare the performance of the proposed methodology with other algorithms (K-means, EM, CURE and MC-DBSCAN). The experimental results demonstrate that the proposed solution has good quality of clustering results.
Authors and Affiliations
A. Alaoui, B. Olengoba Ibara, B. Ettaki, J. Zerouaoui
Keywords
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- EP ID EP240663
- DOI 10.14569/IJACSA.2017.081125
- Views 108
- Downloads 0
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