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Parham Moradi

Parham Moradi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 654
Faculty: Faculty of Engineering
Address: Department of Computer Engineering, Faculty of Engineering, University of Kurdistan
Phone:

Research

Title
Density peaks clustering based on density backbone and fuzzy neighborhood
Type
JournalPaper
Keywords
Fuzzy kernel Density peaks clustering Noise detection Label propagation
Year
2020
Journal PATTERN RECOGNITION
DOI
Researchers Parham Moradi ، Hamid Beigy ، Abdulrahman Lotfi

Abstract

Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-iterative process. However, DPC and most of its improvements suffer from the following shortcomings: (1) highly sensitive to its cutoff distance parameter, (2) ignoring the local structure of data in computing local densities, (3) using a crisp kernel to calculate local densities, and (4) suffering from the cause of chain reaction. To address these issues, in this paper a new method called DPC-DBFN is proposed. The proposed method uses a fuzzy kernel for improving separability of clusters and reducing the impact of outliers. DPC-DBFN uses a density-based kNN graph for labeling backbones. This strategy prevents the chain reaction and effectively assigns true labels to those instances located on the border regions to effectively cluster data with various shapes and densities. The DPC-DBFN is evaluated on some real-world and synthetic datasets. The experimental results show the effectiveness and robustness of the proposed algorithm.