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- W2534954018 abstract "For a large number of high-dimensional data, the traditional DBSCAN algorithms have been unable to cope with, because the traditional DBSCSN algorithm is achieved by calculating the density, but in high-dimensional data, the most of distance between point pairs are similar, they can not be calculated. So the traditional DBSCAN algorithm does not fit the high-dimensional data. In this paper, we put the traditional DBSCAN algorithm as a foundation, proposing a clustering algorithm based on entropy with the MapReduce framework. First, we use space partitioning and simple clustering method to preprocess the data, forming a large number of micro-clusters, and then we use some four-tuples to denote these micro-clusters, then we propose a new formula to calculate the information entropy, which is calculating the information entropy with the consideration of distance between micro-clusters. According to this formula to calculate the entropy of information after the merging of different micro-clusters, in order to choose the best merged objects, then we can avoid the detect that the traditional DBSCAN algorithm requires the user to customize the parameters, We also have added some sort optimization and data storage optimization to improve the efficiency of the algorithm. Finally, through a lot of experiments we demonstrated our algorithm in the MapReduce framework has improved a lot on the efficiency of the algorithm and the final clustering effect." @default.
- W2534954018 created "2016-10-28" @default.
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- W2534954018 date "2016-08-01" @default.
- W2534954018 modified "2023-09-24" @default.
- W2534954018 title "Clustering very large high-dimentional datasets based entropy with MapReduce" @default.
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- W2534954018 doi "https://doi.org/10.1109/fskd.2016.7603351" @default.
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