A Novel Model for Mining Frequent Patterns Based on Embedded Granular Computing

Abstract

For mining frequent patterns, it is very expensive for the Apriori mining model to read the database repeatedly, and a highly condensed data structure made the FP-growth mining model cost larger memory. In order to avoid the disadvantages of these data mining model, this paper proposes a novel data mining model for discovering frequent patterns, called a data mining model based on embedded granular computing, which is different from the Apriori model and the FP-growth model. The data mining model adopts efficiently dividing and conquering from granular computing, which can construct adaptively different hierarchical granules. To form the data mining model, an embedded granular computing model is proposed in this paper. The granular computing model is used in discovering frequent patterns, on the one hand, it avoids reading the database repeatedly via constructing the extended information granule, and lessen the calculated amount of support; on the other hand, it reduces the memory requirements by the attribute granule, where the search space can compress the memory space of data structure that make the method of generating the candidate become simple relatively; and it can divide the overlarge computing task into several easy operations via the attribute granule, namely, the embedded granular computing model could short the size of the search space from a super state to several sub-states. All experimental results show that the data mining model based on embedded granular computing is more reasonable and efficient than these classical models for mining frequent patterns under these different types of datasets. Otherwise, an extra discussion describes the performance trend of the model by a group of experiments.