Uncertainty-Driven Pattern Mining on Incremental Data for Stream Analyzing Service

Abstract

Pattern mining, one of the data analysis approaches, provides meaningful assistance for various business services, such as product recommendation and marketing. However, certain real-world data contain uncertain characteristics, and some business services want to consider the uncertainty of data. Uncertain pattern mining is an advanced technique for discovering more useful patterns from uncertainty-driven data with uncertain information about items. However, although many business services create and process incremental data in real-time, most of the previous uncertain pattern mining techniques have limitations in analyzing incremental data since they mainly focus on processing static data. To address the limitations, we present a list-based uncertain pattern mining method that effectively analyzes incremental uncertainty-driven data in real time by scanning stream data only once. In addition, uncertainty-driven data analytics can be executed efficiently due to the list structure that is effective in construction and mining. The tests of performance for runtime, memory consumption, and scalability are performed using real datasets and synthetic datasets, which illustrate that the suggested technique reveals outstanding performance compared to state-of-the-art algorithms. The additional case study evaluations with concept-drifting tests as well as accuracy and significance tests demonstrate the practical applications of the algorithm and the quality of the extracted results.