Frequent itemset mining of uncertain data streams using the damped window model

Leung, Carson K.; Jiang, Fan

doi:10.1145/1982185.1982393

Cited by 40 publications

(17 citation statements)

References 22 publications

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“…This is a false-negative -oriented approach, and it also uses FP-tree structure to mine frequent patterns from uncertain data streams. The performance of the TFUHS-Stream algorithm is compared with DUF-Streaming algorithm [15], which is a FP-growth-based, false-negative -oriented, TF model to mine frequent patterns from uncertain data streams. This is also similar to the TUF-Streaming algorithm proposed by the same authors [16].…”

Section: Resultsmentioning

confidence: 99%

“…Very limited work has been done to address FPM in uncertain data streams, and either they are built-on a tree-based approach [15–17] or they are limited to mine frequent patterns only from the most recent window [14,29]. In this paper, we present two false-positive -oriented, hyper-structure-based algorithms to mine frequent patterns from an uncertain data stream.…”

Section: Introductionmentioning

confidence: 99%

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Hyper-structure mining of frequent patterns in uncertain data streams

et al. 2012

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyper-structure mining of frequent patterns in uncertain data streams

et al. 2012

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“…These kinds of data share in common the property of being modeled in terms of graph-structured data so that graph streams 1,2,10 are generated. In order to be able to make sense of streaming data, stream mining algorithms are needed 9,24 . When comparing with the mining from traditional static databases, mining from dynamic data streams is more challenging due to the following properties of data streams.…”

Section: Introductionmentioning

confidence: 99%

Effectively and Efficiently Mining Frequent Patterns from Dense Graph Streams on Disk

Braun

Cameron

Cuzzocrea

et al. 2014

Procedia Computer Science

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In this paper, we focus on dense graph streams, which can be generated in various applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. We also investigate the problem of effectively and efficiently mining frequent patterns from such streaming data, in the targeted case of dealing with limited memory environments so that disk support is required. This setting occurs frequently (e.g., in mobile applications/systems) and is gaining momentum even in advanced computational settings where social networks are the main representative. Inspired by this problem, we propose (i) a specialized data structure called DSMatrix, which captures important data from dense graph streams onto the disk directly and (ii) stream mining algorithms that make use of such structure in order to mine frequent patterns effectively and efficiently. Experimental results clearly confirm the benefits of our approach.

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“…However, nowadays, data in many real-life biological, medical or life science applications are riddled with uncertainty [21], [23], [32]. The presence or absence of items in a dataset in these applications is uncertain partially due to inherent measurement inaccuracies and sampling errors (e.g., in sensors or laboratory equipment), human reaction time, and intentional blurring of data to preserve anonymity (e.g., preserve patient's privacy).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

Leung

MacKinnon

Jiang

2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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High volumes of uncertain data can be generated in distributed environments in many real-life biological, medical, and life science applications. As an important data mining task, frequent pattern mining helps discover frequently co-occurring items, objects, or events from these distributed databases. However, users may be interested in only some small portions of all the frequent patterns that can be mined from these databases. In this paper, we propose an intelligent computing system that (i) allows users to express their interests via the use of userspecified constraints and (ii) effectively exploits anti-monotonic properties of user-specified constraints and efficiently discovers frequent patterns satisfying these constraints from the distributed databases containing uncertain data.

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Frequent itemset mining of uncertain data streams using the damped window model

Cited by 40 publications

References 22 publications

Hyper-structure mining of frequent patterns in uncertain data streams

Hyper-structure mining of frequent patterns in uncertain data streams

Effectively and Efficiently Mining Frequent Patterns from Dense Graph Streams on Disk

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

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