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In some real-world applications, the predefined features are not discriminative enough to represent well the distinctiveness of different classes. Therefore, building a more well-defined feature space becomes an urgent task. The main goal of feature space transformation is to map a set of features defined in a space into a new more powerful feature space so that the classification based on the transformed...
Previous studies indicate that FP-Growth has a fast performance while Apriori-TFP is more efficient in terms of used memory. Based on those characteristics, in this paper, we proposed an Apriori-TFP based incremental frequent pattern mining algorithm that can search efficiently within limitation of memory and the further classification work base on those patterns. Especially, the concept of pre-infrequent...
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