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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia

Author 1: A. Alaoui
Author 2: B. Olengoba Ibara
Author 3: B. Ettaki
Author 4: J. Zerouaoui

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.081125

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 11, 2017.

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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.

Keywords: MC-DBSCAN; iterative process; inter-cluster inertia; unsupervised precision-recall metrics

A. Alaoui, B. Olengoba Ibara, B. Ettaki and J. Zerouaoui, “A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081125

@article{Alaoui2017,
title = {A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081125},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081125},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {11},
author = {A. Alaoui and B. Olengoba Ibara and B. Ettaki and J. Zerouaoui}
}


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