Mendeley
TY  _  JOUR
ID  -  139609081015119391
TI  -  Proposing a Density-Based Clustering Algorithm with Ability to Discover Multi-Density Clusters in Spatial Databases
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Zadedehbalaei Ali
AU  -  bagheri alireza
AU  -   Afshar H.
AD  -  
AD  -  
AD  -  دانشگاه صنعتی امیرکبیر
Y1  -  2017
PY  -  2017
VL  -  3
IS  -  
SP  -  171
EP  -  186
KW  -  Multi-density
density-based clustering
spatial data mining
DBSCAN
DO  -  
N2  -  Clustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits suffers from some issues such as difficulty in determining appropriate values for input parameters and inability to detect clusters with different densities. 
In this paper, we introduce a new clustering algorithm which unlike DBSCAN algorithm, can detect clusters with different densities. This algorithm also detects nested clusters and clusters sticking together. The idea of the proposed algorithm is as follows. First, we detect the different densities of the dataset by using a technique and Eps parameter is computed for each density. Then DBSCAN algorithm is adapted with the computed parameters to apply on the dataset. The experimental results which are obtained by running the suggested algorithm on standard and synthetic datasets by using well-known clustering assessment criteria are compared to the results of DBSCAN algorithm and some of its variants including VDBSCAN, VMDBSCAN, LDBSCAN, DVBSCAN and MDDBSCAN. All these algorithms have been introduced to solve the problem of multi-density data sets. The results show that the suggested algorithm has higher accuracy and lower error rate in comparison to the other algorithms.

UR  - rimag.ir/en/Article/28270
L1  - rimag.ir/en/Article/Download/28270
ER  -  
