AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

Aida Chefrour, Labiba Souici-Meslati


Density-based spatial clustering of applications with noise (DBSCAN) is a fundament algorithm for density-based 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 AMF-IDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMF-IDBSCAN algorithm uses a canopy clustering algorithm to pre-clustering datasets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for post-clustering 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 

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