An Improved Numerical DBSCAN Algorithm Based on Non-IIDness Learning
نویسندگان
چکیده
In clustering algorithm research, objects, attributes and other aspects of data sets are usually considered to be independent identically distributed; that is, each object is assumed an uniformly distributed individual with no impacts between objects. However, objects in real life often neither independently nor they non-IID, leading a complex coupling relationship interact other. The results under identical distribution may incomplete or even misleading. To make the DBSCAN as accurate possible, improved numerical based on non-IIDness learning proposed this paper. calculates obtain potential determines parameters Eps xmlns:xlink="http://www.w3.org/1999/xlink">MinPts by characteristics data. Experiments large-scale synthetic show achieves higher accuracy than original main algorithms upon it.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3081500