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Among the various clustering algorithms, DBSCAN is an effective clustering algorithm used in many applications. It has various advantages like no a priori assumption needed about the number of clusters, can find arbitrarily shaped clusters and can perform well even in the presence of outliers. However, the performance is seriously affected when the dataset size becomes large. Moreover, the selection of the two input parameters, Eps and MinPts, has a great impact on the clustering performance. To solve these two problems, this paper modifies the traditional DBSCAN algorithm in two manners.
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