Anytime Frequent Itemset Mining of Transactional Data Streams

Goyal, Poonam; Challa, Jagat Sesh; K., Shrivastava, S.; Goyal, Navneet

doi:10.1016/j.bdr.2020.100146

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…After the first algorithm for mining frequent itemsets in a stream has been presented [23] the real-time data mining becomes an important research field and many new methods [37][38][39][40] were developed for data stream mining. is review of a related work is concentrated on several recent algorithms for mining frequent itemsets; it includes FP-tree, Cantree, DSTree, CPStree, and Cantree-Gtree approaches.…”

Section: Related Workmentioning

confidence: 99%

LCTree-Based Approach for Mining Frequent Items in Real-Time

Chen

Zhong

et al. 2022

Computational Intelligence and Neuroscience

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With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications.

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Section: Related Workmentioning

confidence: 99%

LCTree-Based Approach for Mining Frequent Items in Real-Time

Chen

Zhong

et al. 2022

Computational Intelligence and Neuroscience

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“…Typically, it uses when analysis of historical data. All frequent patterns generated from the whole data stream are considered [22][15], [23] [25].…”

Section: A Landmark Window Modelmentioning

confidence: 99%

Frequent Pattern Mining over Streaming Data: From models to research challenges

Saad¹,

Salem

Abdelkader

2021

IJCI. International Journal of Computers and Information

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Research in frequent pattern mining from streaming data becomes a pioneer in the field of information systems. The data stream is a continuous flow of data generated from different sources. Extracting frequent patterns from streaming data raises new challenges for the data mining community. We present an overview of the growing field of data streams. Many applications handle streaming data such as sensor networks, traffic management, log data, telephone call records, and social networks. These applications generate high volumes of streaming data with velocity, which is difficult to handle with traditional data mining techniques. This paper mainly reviewed different research algorithms, scientific practices, and methods that have been developed for mining frequent patterns from streaming data. In addition, it discusses well-known open-source software and tools for data stream mining, which are developing to handle streaming data. Finally, it summarizes the open issues and challenges to current existing approaches while handling and processing data streams in realworld applications.

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“…(iii) Time fading/damped window [25,26,64] where the transactions are associated with differernt weights, and the more rencent transactions have a higher weight than older ones. With…”

Section: Related Workmentioning

confidence: 99%

A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

Wang

Cong

Zhang

et al. 2021

ACM Trans. Knowl. Discov. Data

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The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

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Anytime Frequent Itemset Mining of Transactional Data Streams

Cited by 14 publications

References 21 publications

LCTree-Based Approach for Mining Frequent Items in Real-Time

LCTree-Based Approach for Mining Frequent Items in Real-Time

Frequent Pattern Mining over Streaming Data: From models to research challenges

A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

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