Towards a variable size sliding window model for frequent itemset mining over data streams

Deypir, Mahmood; Sadreddini, Mohammad Hadi; Hashemi, Sattar

doi:10.1016/j.cie.2012.02.008

Cited by 48 publications

(28 citation statements)

References 23 publications

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“…An itemset which is also known as set of items is frequent in a database if the number of its occurrences in the database is more than or equal to a user specified threshold. This threshold is specified by the user of the mining process [9]. The fig.1 shows frequent mining of items.…”

Section: Updating Data Streammentioning

confidence: 99%

“…Concept change can be categorized in two main categories, concept drift and concept shift. Concept drift describes a gradual change of the concept and concept shift happens when a change between two concepts is more abrupt [9].…”

Section: Updating Data Streammentioning

confidence: 99%

“…proposed algorithm VSW (variable Sliding Window) [9] where the window size is dynamically adjusted based on the amount of the concept change arriving within the data stream. The window expands as the concept becomes stable and shrinks when a change in concept detected.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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.

show abstract

Section: Updating Data Streammentioning

confidence: 99%

Section: Updating Data Streammentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Mining Frequent Itemsets over Data Streams

Maurya¹,

Ambhore²,

Parit³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Therefore, for mining streaming data, fixed sliding window is difficult to adapt to the data stream implied concept drift characteristics. Some variable sliding window based algorithms are proposed, such as VSW algorithm [7] and MFW algorithm [8] . VSW algorithm proposed a variable sliding window model, where the window size measure is actively adjusted based on the detection of concept drift within the data stream.…”

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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“…Mahmood Deypir et.al. given concept of VSW (variable Sliding Window) [17] where the window size measure is actively adjusted based on the volume of the nature variation incoming within the data stream. The window grows as the concept is fixed and gets smaller when variation in concept captured.…”

Section: Related Workmentioning

confidence: 99%

Kalman Filter based Flexible Sliding Window Algorithm for Mining Frequent Itemset over Data Stream

Maurya¹,

K.²

2015

IJCA

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A data stream mining is relevant issue in the context of information gain. The data arrived are large in amount limitless and high rate with time impractical to stock, excavating and testimony at identical measure of time to retrieve intelligence. Sliding window model utilized for frequent pattern mining data stream mining emphasis on recent data and minimum space consumed. In past algorithm window measurement change was steady to concept variation when stagnant and gets smaller when the concept variation happens. Renewed frequent patterns are moderately kept in the current concept whereas the stable transaction is moved out of window. Panes steadily combined to window and performing unnecessary mining for frequent itemsets, conduct is diminishing. Based on the sliding window model the new algorithm named KF_FSW (Kalman Filter based Flexible Sliding Window Model) which utilize Kalman filter function for prediction and measurement approach. The prediction and measurement method is done on basis of already existing information as measure. Thus coagulating the error for accurate position of behavior variation in window size fluctuates in streamed database. Test on standard dataset reports that proposed algorithm coagulates less number of windows for mining and even predicting efficiently the number of count for change ratio captured by occurring change variation.

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Towards a variable size sliding window model for frequent itemset mining over data streams

Cited by 48 publications

References 23 publications

A Survey on Mining Frequent Itemsets over Data Streams

A Survey on Mining Frequent Itemsets over Data Streams

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

Kalman Filter based Flexible Sliding Window Algorithm for Mining Frequent Itemset over Data Stream

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