Title :
Hybrid clustering algorithm
Author_Institution :
Indian Inst. of Technol. Delhi, Delhi, India
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;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346251