Mining frequent itemsets over data streams using efficient window sliding techniques

Li, Hua-Fu; Lee, Suh-Yin

doi:10.1016/j.eswa.2007.11.061

Cited by 160 publications

(84 citation statements)

References 20 publications

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“…al. gave an effective bit-series based, single-pass algorithm, called MFI-Trans-SW (Mining Frequent Itemsets within a Transaction-sensitive Sliding Window) [15], to mine set of frequent itemsets from data streams within a transaction-sensitive sliding window which consists of a fixed number of transactions. The proposed algorithm shows accurate results, runs faster and consumes less memory comparatively then previous algorithms.…”

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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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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“…Mining in data streams incurs extra challenges. [6], to mine itemsets frequency from data streams in a transaction-sensitive sliding window which holds a fixed number of transactions. The proposed algorithm depicts accuracy, faster processing and less memory consumption.…”

Section: Updating Data Streammentioning

confidence: 99%

“…Data streams mining is the most demanding problems in data mining. Real time applications produce large amount of data streams, such as sensor data generated from sensor networks, online transaction flows in retail chains, Web record and click-streams in Web applications, call records in telecommunications, and performance measurement in network monitoring and traffic management [6]. Following data mining requirement must be satisfied:…”

Section: Introductionmentioning

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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“…Data stream mining is one of the most challenging fields of data mining. Identifying and discovering the recent knowledge and pattern can provide valuable information for the analysis of the data stream [34]. DSMSs are effective tools for building sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Load Shedding using Window Aggregation Queries on Data Streams

Senthamilarasu¹,

Hemalatha²

2012

IJCA

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The processes of extracting knowledge structures for continuous, rapid records are known as the Data Stream Mining. The main issue in stream mining is handling streams of elements delivered rapidly which makes it infeasible to store everything in active storage. To overcome this problem of handling voluminous data we exposed a novel load shedding system using window based aggregate function of the data stream in which we accept those tuples in the stream that meet a criterion. Accepted tuples are conceded to another process as a stream, while further tuples are dropped. This proposed model conceivably segregates the data input stream into windows and probabilistically decides which tuple to drop based on the window function. The best window aggregate function used for dropping tuples is identified with the three prediction models used in data mining they are Decision Tree, Naïve Bayes and Logistic Regression. The result shows that the cumulative distance and density rank functions outperforms the remaining methods. Distinct to prior methods, our method preserves uniformity of windows all over a query plan, and constantly distributes subsets of the original query responds with insignificant denial in the excellence of the consequence.

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Mining frequent itemsets over data streams using efficient window sliding techniques

Cited by 160 publications

References 20 publications

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

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

A Survey on Mining Frequent Itemsets over Data Streams

Load Shedding using Window Aggregation Queries on Data Streams

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