抽象的な

IMPROVEMENT OF CLUSTERING ON HIGH DIMENSIONAL DATASETS USING PCA

Dr.Dharmender Kumar, Parveen Kumar

The last decade has seen an explosive growth in the generation and collection of data. In the field of data mining there are various techniques are used to extract useful information from the data set. There is various estimation techniques are used in clustering methods out of these Euclidean distance and density is used for estimation. Out of these estimation techniques one another technique mass is also used. Mass based clustering gives better performance when apply with PCA technique in multidimensional and high dimensional data set. On the basis of run time behaviour the DEMassDBSCAN algorithm is better than DBSCAN clustering algorithm is discussed.