AMFIDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique Densitybased spatial clustering of applications with noise (DBSCAN) is a fundament algorithm for densitybased clustering. It can discover clusters of arbitrary shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to treat large datasets, to attend to outperforming when new data objects are inserted into the existing database, to remove totally a noise points and outliers and to handle the local density variation that exists within the cluster. So, a good clustering method should allow a significant density modification within the cluster and should learn a dynamics and large databases. In this paper, an enhancement of the DBSCAN algorithm is proposed based on incremental clustering called AMFIDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMFIDBSCAN algorithm uses a canopy clustering algorithm to preclustering datasets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for postclustering to reduce the number of outliers by replacing noises by chosen medians. Experimental results are obtained from the University California Irvine (UCI) repository UCI data sets. The final results show that our algorithm get good results with respect to the famous DBSCAN, IDBSCAN, and DMDBSCAN

Automatic Clustering by Detecting Significant Density Dips in Multiple Dimensions Clustering algorithms are used to find groups of similar items in a dataset. Automatic clustering algorithms achieve this task without requiring users to input critical parameters. A recent automatic clustering methodology uses Hartiganu0027s dip test to detect significant peaks in the distribution of a dataset. This test can detect peaks in the distribution of a onedimensional variable. To perform clustering in multiple dimensions, algorithms of this methodology rely on onedimensional transformations of the dataset, which limits their effectiveness. In this paper, we present MDip, an automatic clustering algorithm that works directly on multidimensional space. MDip also assumes that clusters correspond to different peaks in the distribution of the dataset. It separates clusters at the dips that form between neighboring peaks. Dips are detected directly in multidimensional space, using a graphbased method. Their statistical significance is evaluated through appropriate simulations. Our experimental evaluation indicates that MDip achieves significantly better results than existing algorithms based on Hartiganu0027s dip, as well as other stateoftheart automatic clustering algorithms.