Adaptive cutoff distance: Clustering by fast search and find of density peaks

摘要

Clustering by fast search and find of density peaks (CFSFDP) was proposed to create clusters by finding high-density peaks, quickly. CFSFDP mainly based on two rules: 1) a cluster center has a high dense point and 2) a cluster center lies at a large distance from other clusters centers. The effectiveness of CFSFDP highly depends upon the cutoff distance (C-d), which is used to estimate the density of each data point. However, there is a need to provide the predefined C-d. In this paper, we propose an adaptive way to estimate the accurate C-d by using the characteristics of Improved Sheather-Jones (ISJ) method named as IJS-CFSFDP. ISJ method provides the best estimation for C-d to measure accurate density of each data point. We perform a number of experiments on standard benchmark clustering datasets and real academic dataset of students. The evaluated clustering results on education dataset validate the IJS-CFSFDP can be used to make intelligent contents delivery system based on the capability and intelligence of the student. The experimental results on synthetic datasets show that the proposed adaptive C-d method creates better clusters as compare to the CFSFDP, mean shift, affinity propagation and k-means.