Unique Neighborhood Set Parameter Independent Density-Based Clustering With Outlier Detection

Md. Anisur Rahman; Kenneth Li-Minn Ang; Kah Phooi Seng

doi:10.1109/ACCESS.2018.2857834

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Unique Neighborhood Set Parameter Independent Density-Based Clustering With Outlier Detection

Journal article

Peer reviewed

Unique Neighborhood Set Parameter Independent Density-Based Clustering With Outlier Detection

Md. Anisur Rahman, Kenneth Li-Minn Ang and Kah Phooi Seng

IEEE Access, Vol.6, pp.44707-44717

2018

DOI: https://doi.org/10.1109/ACCESS.2018.2857834

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Abstract

clustering

density-based

outlier detection

unique closest neighbour

unique neigborhood set

Machine learning algorithms such as clustering, classification, and regression typically require a set of parameters to be provided by the user before the algorithms can perform well. In this paper, we present parameter independent density-based clustering algorithms by utilizing two novel concepts for neighborhood functions which we term as unique closest neighbor and unique neighborhood set. We discuss two derivatives of the proposed parameter independent density-based clustering (PIDC) algorithms, termed PIDC-WO and PIDC-O. PIDC-WO has been designed for data sets that do not contain explicit outliers whereas PIDC-O provides very good performance even on data sets with the presence of outliers. PIDC-O uses a two-stage processing where the first stage identifies and removes outliers before passing the records to the second stage to perform the density-based clustering. The PIDC algorithms are extensively evaluated and compared with other well-known clustering algorithms on several data sets using three cluster evaluation criteria (F-measure, entropy, and purity) used in the literature, and are shown to perform effectively both for the clustering and outlier detection objectives.

Details

Title: Unique Neighborhood Set Parameter Independent Density-Based Clustering With Outlier Detection
Authors: Md. Anisur Rahman (Author) - Charles Sturt University
Kenneth Li-Minn Ang (Author) - Charles Sturt University
Kah Phooi Seng (Author) - Charles Sturt University
Publication details: IEEE Access, Vol.6, pp.44707-44717
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2018
DOI: 10.1109/ACCESS.2018.2857834
ISSN: 2169-3536; 2169-3536
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513906402621
Output Type: Journal article

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Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications