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Research on multi-scale data mining method. (Chinese. English summary) Zbl 1374.68147
Summary: Many researches of data mining have paid close attention to multi-scale theory. However the study of multi-scale data mining still comes short on universal theories and approaches. To overcome this limitation, this paper conducts a study of universal multi-scale data mining on theoretical and methodological aspect. First, the paper lays out the definition of data-scale-partition and data-scale based on concept hierarchy, and characterizes the relationship of upper-layer and lower-layer datasets between multi-scale datasets. Next, it illustrates the definition and essence of multi-scale data mining, and presents the classification of multi-scale data mining methods. Finally, it introduces the algorithm framework and its theoretical basis of multi-scale data mining, and proposes an algorithm named MSARMA (multi-scale association rules mining algorithm) to realize the transition of knowledge in multi-scale data expressions. Experiments are carried out to test MSARMA with the help of IBM T10I4D100K dataset and demographic dataset from H province. The results indicate that MSARMA is effective and feasible with better coverage rate, better accuracy and lower average support error.
68P15 Database theory
68T05 Learning and adaptive systems in artificial intelligence
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