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JACIII Vol.17 No.1 pp. 60-73
doi: 10.20965/jaciii.2013.p0060
(2013)

Paper:

Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood

Hao Liu, Satoshi Oyama, Masahito Kurihara,
and Haruhiko Sato

Division of Synergetic Information Science in Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan

Received:
August 29, 2012
Accepted:
November 15, 2012
Published:
January 20, 2013
Keywords:
clustering, fuzzy neighborhood function, landmarks, DBSCAN, FN-DBSCAN
Abstract

Clustering is an important tool for data analysis and many clustering techniques have been proposed over the past years. Among them are density-based clustering methods, which have several benefits such as the number of clusters is not required before carrying out clustering; the detected clusters can be represented in an arbitrary shape and outliers can be detected and removed. Recently, the density-based algorithms were extended with the fuzzy set theory, which has made these algorithm more robust. However, the density-based clustering algorithms usually require a time complexity of O(n2) where n is the number of data in the data set, implying that they are not suitable to work with large scale data sets. In this paper, a novel clustering algorithm called landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN) is proposed. The concept, landmark, is used to represent a subset of the input data set which makes the algorithm efficient on large scale data sets. We give a theoretical analysis on time complexity and space complexity, which shows both of them are linear to the size of the data set. The experiments show that the landmark FN-DBSCAN is much faster than FN-DBSCAN and provides a very good quality of clustering.

Cite this article as:
H. Liu, S. Oyama, M. Kurihara, and <. Sato, “Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.1, pp. 60-73, 2013.
Data files:
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Last updated on May. 31, 2020