NDLI: A density based algorithm for discovering clusters with varied density

Content Provider	IEEE Xplore Digital Library
Author	Louhichi, S. Gzara, M. Ben Abdallah, H.
Copyright Year	2014
Description	Author affiliation: High Sch. of Comput. Sci. & Math., Univ. of Monastir, Monastir, Tunisia (Louhichi, S.; Gzara, M.) \|\| King Abdulaziz Univ., Jeddah, Saudi Arabia (Ben Abdallah, H.)
Abstract	Clustering is a well studied problem in data analysis and data mining. It has many areas of applications and it is used as a preprocessing step before other data mining tasks such as classification and association analysis. Discovering clusters of arbitrary shapes is a challenging task. Even though density based clustering algorithms manage to detect clusters with different shapes and sizes in large data bases with the presence of noise, they fail in handling local density variation within the data. In this paper, we propose a new algorithm based on the well known density based clustering algorithm DBSCAN. Our algorithm approximates the k nearest neighbors curve by spline interpolation and uses mathematic properties of functions to detect automatically points where the function changes concavity. Some of these points corresponds to the different levels of density within the data set. Experimental results on synthetic data sets show the efficiency of the proposed approach.
Starting Page	1
Ending Page	6
File Size	402427
Page Count	6
File Format	PDF
ISBN	9781479933501
e-ISBN	9781479933518
DOI	10.1109/WCCAIS.2014.6916622
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-17
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Interpolation Shape Noise Clustering algorithms DBSCAN Density based clustering Partitioning algorithms Data mining Splines (mathematics)
Content Type	Text
Resource Type	Article

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