New weighted clustering ensemble based on external index and subspace attributes partitions for large features datasets. (1st August 2019) - Khatir, Nadjia; Nait-Bahloul, Safia
Real world datasets are commonly large and involve a lot of features. This is due because of the variety of domains where are obtained from or for the impact of diverse features extractors techniques. Relatively few works on selecting and weighting relevant features for the propose of clustering data are involved in the literature. To cope with this issue, in this paper a new weighting partitions-based features selection framework is proposed in conjunction with clustering ensemble for large features datasets. Six real world datasets from both images and biological domains are chosen to be evaluated and an average accuracy between 75.18% and 98.04% is achieved. Results show that the new proposed technique has been successfully outclassed state-of-the-art methods in term of both effectiveness and efficiency.