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Mining closed frequent item set(CFI) plays a fundamental role in many real-world data mining applications. However, memory requirement and computational cost have become the bottleneck of CFI mining algorithms, particularly when confronting with large scale datasets, which herewith makes mining closed frequent item set from large scale datasets a significant and challenging issue. To address the above...
Mining closed frequent itemset (CFI) plays an essential role in many real-world data mining applications. With the emergence of abundant large-scale data sets, it now turns to be a significant and challenging issue to mine CFI concurrently. This paper proposes a parallel balanced mining algorithm for CFI based on the MapReduce platform. The proposed algorithm adopts Greedy strategy to group items...
This is a article of research which bases on the classical granular computing and the Association Rules, focus on the association rules and decision-making rules of the information system. Firstly, defines a association-rule characteristic Information Granule, which can be treated as a sub-definition of the Information Granule, and a association-rule characteristic Information Granularity matirx is...
In this paper, a novel data driven knowledge extraction scheme is proposed and applied to realize power system stability estimation since power system stability assessment can be treated as a typical classification problem (stable/unstable). The strategy is composed of three cascading layers, including the feature selection for choosing an optimal subset from candidate inputs, pattern discovery layer...
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