Romanian Journal of Information Science and Technology (ROMJIST)

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ROMJIST is a publication of Romanian Academy,
Section for Information Science and Technology

Editor – in – Chief:
Academician Dan Dascalu

Secretariate (office):
Adriana Neagu
Adress for correspondence: romjist@nano-link.net (after 1st of January, 2019)

Editing of the printed version: Mihaela Marian (Publishing House of the Romanian Academy, Bucharest)

Technical editor
of the on-line version:
Lucian Milea (University POLITEHNICA of Bucharest)

Sponsors:
• National Institute for R & D
in Microtechnologies
(IMT Bucharest), www.imt.ro
• Association for Generic
and Industrial Technologies (ASTEGI), www.astegi.ro

ROMJIST Volume 22, No. 3-4, 2019, pp. 244-258, Paper no. 631/2019
 

Ahmed FAHIM
Clustering Algorithm for Multi-density Datasets

ABSTRACT: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is density-based clustering method. It discovers clusters with varied shapes, sizes and handles noise. But it fails to discover clusters of varied density. This problem arises due to its dependency on global parameters especially Eps (represents neighborhood radius for each point in dataset). This paper introduces very simple idea to deal with this problem. The idea is steamed from density-based methods especially DENCLUE (DENsity-based CLUstEring), DBSCAN algorithm and k-nearest neighbors. The proposed method estimates local density -for each point in dataset- as the sum of distances to the k-nearest neighbor, arranges points in ascending order based on local density. The algorithm starts the clustering process from the highest density point by adding un-clustered points that have similar density as first point in cluster. Similar means there is small variance in density between the current point and the first point in cluster. Also, the point is assigned to current cluster if the sum of distances to its Minpts-nearest neighbors is less than or equal to the density of first point (core point condition in DBSCAN). Experimental results show the efficiency of the proposed method in discovering varied density clusters from data.

KEYWORDS: Clustering methods; Data analysis; Data mining; Knowledge discovery; Un-supervised learning

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