ISVS3CE: Incremental support vector semi-supervised subspace clustering ensemble and enhanced bat algorithm (ENBA) for high dimensional data clustering

Abstract

In the recent work, Incremental Soft Subspace Based Semi-Supervised Ensemble Clustering (IS4EC) framework was proposed which helps in detecting clusters in the dataset. IS4EC framework also increases the results of clustering by reducing the intra-cluster distance and increasing the inter-cluster distance with increased cluster quality. It cannot attain acceptable results while handling high dimensional data. However, decreasing the dimensional subspace becomes extremely difficult issue. In IS4EC framework, to choose the optimal ensemble members also extremely becomes challenging issue. In order to solve these issues of traditional cluster ensemble methods, first propose an Incremental Support vector Semi-Supervised Subspace Clustering Ensemble (ISVS3CE) framework which makes utilized of benefits of the random subspace algorithm and the Constraint Propagation (CP) algorithm. Here the centroid values were selected by using the Support Vector Machine (SVM) classifier. In the ISVS3CE framework, Incremental Ensemble Member Chosen (IEMC) process is performed by using the ENhanced Bat Algorithm (ENBA), and the normalized cut algorithm is introduced to perform high dimensional data clustering. The ISVS3CE framework is successful for solving high dimensional data issue, at the same time as the CP algorithm is valuable for incorporating the prior information. Results demonstrate that the proposed ISVS3CE framework performs well on datasets by means of very high dimensionality, and better than the traditional clustering ensemble methods.