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- W3089773089 abstract "Density-based clustering plays a crucial role in various applications and realizes clusters with arbitrary contour. In case of huge amount of data generation, the clustering analysis plays an important role in big data mining. But no universal clustering algorithm is available to solve all problems universally. Usually, the algorithms were designed as per requirement which was based on certain assumptions. This paper focuses on attempts to remove noise parameter from existing algorithm and propose an Enhanced Shuffling DBSCAN (ESDBSCAN) algorithm which outperforms on legacy DBSCAN. ESDBSCAN is better than any other modified version of DBSCAN which gives better performance in terms of run time complexity, when runs in a distributed environment. The paper proposes a proficient and enhanced distributed clustering algorithm using existing data partition on Map-Reduce platform. The ESDBSCAN algorithm has minimum training error and faster than the existing algorithm." @default.
- W3089773089 created "2020-10-08" @default.
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- W3089773089 date "2020-10-02" @default.
- W3089773089 modified "2023-09-27" @default.
- W3089773089 title "ESDBSCAN: Enhanced Shuffling Based Density Clustering" @default.
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- W3089773089 doi "https://doi.org/10.1007/978-981-15-6014-9_71" @default.
- W3089773089 hasPublicationYear "2020" @default.
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