Sliding window-based frequent pattern mining over data streams

Tanbeer, Syed Khairuzzaman; Ahmed, Chowdhury Farhan; Jeong, Byeongmoon; Lee, Young-Koo

doi:10.1016/j.ins.2009.07.012

Cited by 133 publications

(76 citation statements)

References 39 publications

(61 reference statements)

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“…However, the construction of such an FP-tree requires two database scans, which is the major limitation of using an FPtree approach for handling a data stream [11].…”

Section: Fp-growth Algorithmmentioning

confidence: 99%

A Survey on Improved Algorithms for Mining Association Rules

Khanali¹,

Vaziri²

2017

IJCA

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Different types of data, needs of users and variety application problems are lead to produce a range of methods to discover patterns and dependent relationships. This application follows a set of association rules according to know which one of set of objects affects on a set of other objects. This association rules predict the occurrence of an object based on the occurrence of other objects. The associative algorithms have the challenge of redundant association rules and patterns, but studying various methods of association rules is expressive that the recent researches focused on solving the challenges of the tree and lattice structures and their compounds about association algorithms. In this paper, the associative algorithms and their function are described, and finally the new improved association algorithms and the proposed solutions to solve these challenges are explained.

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“…However, the construction of such an FP-tree requires two database scans, which is the major limitation of using an FPtree approach for handling a data stream [11].…”

Section: Fp-growth Algorithmmentioning

confidence: 99%

A Survey on Improved Algorithms for Mining Association Rules

Khanali¹,

Vaziri²

2017

IJCA

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“…Frequent itemsets are mined using the FP-Growth method when the user requests them. Another recently proposed prefix tree based algorithm is the CPS-Tree [12] which is superior to the DSTree. This method is similar to the DSTree algorithm but dynamically reconstructs the prefix tree to maintain support descending order of nodes.…”

Section: Related Workmentioning

confidence: 99%

A variable size sliding window based frequent itemsets mining algorithm in data stream

Wang

2017

AIP Conference Proceedings

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A software safety assessment method based on the combination of frequency analysis method and analytic hierarchy process AIP Conference Proceedings 1839, 020158 (2017) Abstract. Due to the unpredictability and the concept drift character of the data stream, the traditional sliding window is difficult to adapt to frequent itemsets mining in data stream. A new variable sliding window based VSW-SCPS algorithm is proposed. The algorithm maintains a tree structure of SCPS-tree in memory, which is the storage structure of sliding window. When data flowing in, the SCPS-tree will adjust dynamically, the window size will be adjusted by the detection of concept drift according to the result of FP-growth mining algorithm. Experimental results show that the proposed algorithm has good time efficiency.

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“…The paper proposed efficient technique Compact Pattern Stream tree (CPS-tree) [8] developed by Tanbeer et. al.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Mining Frequent Itemsets over Data Streams

Maurya¹,

Ambhore²,

Parit³

2017

IJCA

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Mining frequent itemsets over data stream has been challenging task. The incoming data from various sources like ecommerce website, click streams, text, audio, weather forecasting etc. are massive unbounded and high speed that it is impractical to store all, process and scan complete data at the same time to extract information. While processing memory and time are the main parameters must be minimum consumed. Thus the paper provides different algorithms for mining over static and dynamic data also known as data stream.

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Sliding window-based frequent pattern mining over data streams

Cited by 133 publications

References 39 publications

A Survey on Improved Algorithms for Mining Association Rules

A Survey on Improved Algorithms for Mining Association Rules

A variable size sliding window based frequent itemsets mining algorithm in data stream

A Survey on Mining Frequent Itemsets over Data Streams

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