UALM: Unsupervised Active Learning Method for clustering low-dimensional data

Article type: Research Article

Authors: Javadian, Mohammad^{a; b; *} | Shouraki, Saeed Bagheri^b

Affiliations: [a] Energy Department, Kermanshah University of Technology, Kermanshah, Iran | [b] Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

Correspondence: [*] Corresponding author. Mohammad Javadian. Tel.: +989123019027; Fax: +982166164051; E-mail: mjavadian@ee.sharif.edu.

Abstract: In this paper the Unsupervised Active Learning Method (UALM), a novel clustering method based on the Active Learning Method (ALM) is introduced. ALM is an adaptive recursive fuzzy learning algorithm inspired by some behavioral features of human brain functionality. UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the connected-component algorithm for finding clusters. Then determines cluster centers by intersecting the narrow-paths. Experimental results confirmed the superiority of our proposed method compared to the two most well-known density-based clustering algorithms, DBSCAN and DENCLUE.

Keywords: Active Learning Method, clustering, density-based clustering, Unsupervised Active Learning Method, fuzzy data

DOI: 10.3233/JIFS-16360

Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2393-2411, 2017