By Topic

DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Smiti, A. ; LARODEC, Univ. of Tunis, Tunis, Tunisia ; Elouedi, Z.

Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. This paper presents an efficient and effective clustering technique, named DBSCAN-GM that combines Gaussian-Means and DBSCAN algorithms. The idea of DBSCAN-GM is to cover the limitations of DBSCAN, by exploring the benefits of Gaussian-Means: it runs Gaussian-Means to generate small clusters with determined cluster centers, in purpose to estimate the values of DBSAN's parameters. The results of our method show that it is efficient even for large data sets especially data with large dimension and capable to handle noises, contrary to partitioning algorithms such as K-Means or Gaussian-Means. Additionally, DBSCAN-GM does not necessitate any priori information, in contrast to the density clustering DBSCAN obliging two input parameters which are hard to guess, namely Eps (the radius that bounds the neighborhood region of an object) and MinPts (the minimum number of objects that must exist in the objects neighborhood region). Simulative experiments are carried out on a variety of datasets, which highlight the DBSCAN-GM's effectiveness and cluster validity to check the good quality of clustering results.

Published in:

Intelligent Engineering Systems (INES), 2012 IEEE 16th International Conference on

Date of Conference:

13-15 June 2012