Sliding window filtering: an efficient method for incremental mining on a time-variant database

Lee, Chang-Hung; Lin, Cheng-Ru; Chen, Ming-Syan

doi:10.1016/j.is.2004.02.001

Cited by 104 publications

(109 citation statements)

References 27 publications

(43 reference statements)

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“…At the end of each scan, transactions that are potentially useful are used for the next iteration. A technique called scan reduction uses candidate 2 item sets to generate subsequent candidate item sets [12]. If all intermediate data can be held in the main memory, only one scan is required to generate all candidate frequent item sets.…”

Section: A Apriori-based Algorithmsmentioning

confidence: 99%

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Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Garg¹,

Sharma²

2011

SpecialIssue

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Section: A Apriori-based Algorithmsmentioning

confidence: 99%

“…However, it still requires multiple scans of the dataset. Another incremental Apriori based algorithm is called Sliding Window Filtering (SWF) [12]. SWF incorporates the main idea of Partition algorithm with Apriori to allow incremental mining.…”

Section: E Incremental Update With Apriori-based Algorithmsmentioning

confidence: 99%

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Garg¹,

Sharma²

2011

SpecialIssue

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“…Previous studies contributed to the efficient mining of frequent itemsets over data streams [2,3,4]. Li et al proposed prefix tree-based singlepass algorithms, DSM-FI and DSM-MFI, to mine the set of all frequent itemsets and maximal frequent itemsets over the history of the data streams [5,6].…”

Section: Related Workmentioning

confidence: 99%

Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Kim¹,

Kim²,

Kim³

2010

Journal of Information Processing Systems

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Abstract-A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

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“…With the emergence of new applications, the data processed may be in the continuous dynamic data stream [7,14,15]. Examples include network traffic analysis, Web click stream mining, network intrusion detection, and on-line transaction analysis.…”

Section: Introductionmentioning

confidence: 99%