NDLI: Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory

Content Provider	IEEE Xplore Digital Library
Author	Smiti, A. Eloudi, Z.
Copyright Year	2013
Description	Author affiliation: LARODEC, Univ. de Tunis, Tunis, Tunisia (Smiti, A.; Eloudi, Z.)
Abstract	Clustering is one of the most valuable methods of computational intelligence field, in which sets of related objects are cataloged into clusters. Almost all of the well-known clustering algorithms require input number of clusters which is hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. In contrast, density based methods, such as DBSCAN, have obvious advantages over explicit samples. They discover the number of clusters, as well as, they detect noises. Additionally, the shape of such clusters can also be irregular. However, they have difficulties in handling the challenges posed by the collection of natural data which is often vague. This paper presents an efficient clustering technique, named “Soft DBSCAN” that combines DBSCAN and fuzzy set theory. Our new method is galvanized by Fuzzy C Means in the way of using the fuzzy membership functions. The results of our method show that it is efficient not only in handling noises, contrary to Fuzzy C Means, but also, able to assign one data point into more than one cluster. Simulative experiments are carried out on a variety of datasets, throughout different evaluation's criteria, which highlight the soft DBSCAN's effectiveness and cluster validity to check the good quality of clustering results.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	380
Ending Page	385
File Size	250054
Page Count	6
File Format	PDF
ISBN	9781467356350
ISSN	21582246
e-ISBN	9781467356374
DOI	10.1109/HSI.2013.6577851
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-06
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Clustering methods Noise Clustering algorithms Linear programming Partitioning algorithms Noise measurement
Content Type	Text
Resource Type	Article

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