A Multi-Criteria Decision Method in the DBSCAN Algorithm for Better Clustering
Abdellah Idrissi, Altaf Alaoui · International Journal of Advanced Computer Science and Applications · 2016
This paper presents a solution based on the unsupervised classification for the multiple-criteria analysis problems of data, where the characteristics and the number of clusters are not predefined, and the objects of data sets are described by several criteria, and the latter can be contradictory, of different nature and varied weights. This work focuses on two different tracks of research, the unsupervised classification which is one of data mining techniques as well as the multi-criteria clustering which is part of the field of Multiple-criteria decision-making. Experimental results on different data sets are presented in order to show that clusters, formed using the improvement of the algorithm DBSCAN by incorporating a model of similarity, are intensive and accurate.