Fuzzy Core DBScan Clustering Algorithm
作者： Gloria Bordogna , Dino Ienco
DOI: 10.1007/978-3-319-08852-5_11
关键词: Computer science 、 CURE data clustering algorithm 、 FLAME clustering 、 Cluster analysis 、 Correlation clustering 、 Canopy clustering algorithm 、 SUBCLU 、 DBSCAN 、 Algorithm 、 OPTICS algorithm
摘要: In this work we propose an extension of the DBSCAN algorithm to generate clusters with fuzzy density characteristics. The original version requires two parameters (minPts and e) determine if a point lies in dense area or not. Merging different areas results into that fit underlined dataset densities. approach, single threshold is employed for all datasets points while distinct same set can exhibit order deal issue, Approx Fuzzy Core applies soft constraint model densities, thus relaxing rigid assumption used algorithm. proposal compared classic DBSCAN. Some are discussed on synthetic data.
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