Efficiently mining erasable stream patterns for intelligent systems over uncertain data

Data mining is a method for extracting useful information that is necessary for a system from a database. As the types of data processed by the system are diversified, the transformed pattern mining techniques for processing these type of data have been proposed. Unlike the traditional pattern mining methods, erasable pattern mining is a technique for finding the patterns that can be removed by coming with a small profit. Erasable pattern mining should be able to process data by considering both the environment that the data are generated from and the characteristics of the data. An uncertain database is a database that is composed of uncertain data. Since erasable patterns discovered from uncertain data contain significant information, these patterns need to be extracted. In addition, databases gradually increase, because the data from various fields is generated and accumulated over data streams. Data streams should be processed as intelligently as possible to provide the useful data to the system in real time. In this paper, we propose an efficient erasable pattern mining algorithm that processes uncertain data that is generated over data streams. The uncertain erasable patterns discovered through the suggested technique are more meaningful information by considering the probability of the item and the profit. Moreover, the proposed method can perform efficient mining operations by using both tree and list structures. The performance of the suggested algorithm is verified through the performance tests compared with state-of-the-art algorithms using real data sets and synthetic data sets.