Affiliations: [a]
College of Information Science and Technology, Beijing Normal University, Beijing, China
| [b]
Department of Computer Science and Information Technology, University of Management Sciences and Information Technology, Kotli, AJK, Pakistan
| [c]
Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
| [d]
Business School, Beijing Normal University, Beijing, China
Correspondence:
[*]
Corresponding author. Rongfang Bie, College of Information Science and Technology, Beijing Normal University,
Beijing 100875, China. Tel.: +86 10 58804050; Fax: +86 10 58807938; E-mail: rfbie@bnu.edu.cn.
Abstract: 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 (Cd), which is used to estimate the density of each data point. However, there is a need to provide the predefined Cd. In this paper, we propose an adaptive way to estimate the accurate Cd by using the characteristics of Improved Sheather-Jones (ISJ) method named as IJS-CFSFDP. ISJ method provides the best estimation for Cd 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 Cd method creates better clusters as compare to the CFSFDP, mean shift, affinity propagation and k-means.
Keywords: Density based clustering, kernel density estimation, optimal cutoff distance selection, Improved Sheather-Jones (ISJ) method
