SPAAT-a modern tree based approach for sequential pattern mining with minimum support

Though we have seen many data-mining methods there are always various discrepancies and we have proposed a frequent pattern mining is an important data-mining for finding the correlations among items. Since the frequencies for various items are always varied, specifying a single minimum support cannot exactly discover interesting patterns. In order to overcome these discrepancies we propose an apriori-based method to include the concept of multiple minimum supports (MMS in short) on association rule mining. It allows user to specify MMS to reflect the different natures of items. Since the mining of sequential pattern may face the same problem, we extend the traditional definition of sequential patterns to include the concept of MMS in this study. For efficiently discovering sequential patterns with MMS, we develop a data structure, named PLMS-tree, to store all necessary information from database. After that, a pattern growth method, named MSCP-growth, is developed to discover all sequential patterns with MMS from PLMS-tree. This paper can be used in evolving scenarios and sure to create a revolution in its own field.