DocumentCode :
2570770
Title :
Hybrid clustering algorithm
Author :
Chandra, B.
Author_Institution :
Indian Inst. of Technol. Delhi, Delhi, India
fYear :
2009
fDate :
11-14 Oct. 2009
Firstpage :
1345
Lastpage :
1348
Abstract :
The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of clusters is generated using K means algorithm. The proposed algorithm consists of two phases, the first phase being constructing the graph and de-associating the graphs into connected sub graphs which denote the number of sub groups within the data. In the second phase in order to group the sub graphs that are close to each other K means algorithm is employed.
Keywords :
data mining; graph theory; pattern clustering; K means algorithm; data mining; density based clustering; graph based clustering algorithm; hybrid clustering algorithm; Clustering algorithms; Cybernetics; Data structures; Iterative algorithms; Machine learning; Machine learning algorithms; Merging; Partitioning algorithms; Sampling methods; USA Councils; Density Based Clustering; Partition Clustering; k-Means;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2009.5346251
Filename :
5346251
Link To Document :
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