title: Hyper-structure mining of frequent patterns in uncertain data streams.
abstract: Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. The current solutions for frequent pattern mining in uncertain streams take a FP-tree-based approach; however, recent studies have shown that FP-tree-based algorithms do not perform well in the presence of data uncertainty. In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. The first algorithm, UHS-Stream, is designed to find all frequent itemsets up to the current moment. The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. Experimental results show that the proposed hyper-structure-based algorithms outperform the existing tree-based algorithms in terms of accuracy, runtime, and memory usage.
authors.affiliation: Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA.
authors.forename: Chandima
authors.initials: C
authors.lastname: Hewanadungodage
authors.affiliation: Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA.
authors.forename: Yuni
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA.
authors.forename: Jaehwan John
authors.initials: JJ
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave., ENB 118, Tampa, FL 33620, USA.
authors.forename: Yi-Cheng
authors.initials: YC
authors.lastname: Tu
keywords: Data mining; Data stream; Data uncertainty; Frequent patterns