Finding recent frequent itemsets adaptively over online data streams

Chang, Joong Hyuk; Lee, Won Suk

doi:10.1145/956804.956807

Cited by 96 publications

(97 citation statements)

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“…Various algorithms [3], [4], [5], [6], [8], [9], [10], [14], [15] have been actively proposed to extract different types of information embedded in a data stream. To minimize the number of monitored item sets in finding frequent item sets over an online data stream, we have proposed the estDec method [5].…”

Section: Related Workmentioning

confidence: 99%

“…To minimize the number of monitored item sets in finding frequent item sets over an online data stream, we have proposed the estDec method [5]. In this method, only significant item sets that are currently frequent or can possibly be frequent in the near future are monitored by a prefix tree structure.…”

Section: Related Workmentioning

confidence: 99%

“…We have proposed the method [11] for finding a set of MFIs over a data stream based on the estDec method [5]. By modifying the internal node structure of a prefix tree used in the estDec method, it is possible to extract all of MFIs without any additional superset/subset checking mechanism while the original estDec method for finding frequent item sets is still working properly.…”

Section: Introductionmentioning

confidence: 99%

“…Á Á Á ! i n in a prefix tree P k represents the item set e. Patterns embedded in a data stream are changed as time goes by [5], [22]. Identifying the recent change of a data stream quickly enables to find their gradual change so that it can be timely utilized.…”

mentioning

confidence: 99%

“…The detailed description of this estimation is presented in [5]. If the count of an item set e is not currently monitored by P k , its approximate count is estimated by C max k ðeÞ.…”

mentioning

confidence: 99%

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estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

Woo

Lee

2009

IEEE Trans. Knowl. Data Eng.

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Frequent item set mining is one of the most challenging issues for descriptive data mining. In general, its resulting set tends to produce a large number of frequent item sets. To represent them in a more compact notation, closed or maximal frequent item sets are often used but finding such item sets over online transactional data streams is not easy due to the requirements of a data stream. For this purpose, this paper proposes a method of tracing the set of MFIs instantly over an online data stream. The method, namely estMax, maintains the set of frequent item sets by a prefix tree and extracts all MFIs without any additional superset/subset checking mechanism. Upon processing a new transaction, those frequent item sets that are matched maximally by the transaction are newly marked in their corresponding nodes of the prefix tree as candidates for MFIs. At the same time, if any subset of a newly marked item set has been already marked as a candidate MFI by a previous transaction, it is cleared as well. By employing this additional step, it is possible to extract the set of MFIs at any moment. The performance of the estMax method is comparatively analyzed by a series of experiments to identify its various characteristics.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The detailed description of this estimation is presented in [5]. If the count of an item set e is not currently monitored by P k , its approximate count is estimated by C max k ðeÞ.…”

mentioning

confidence: 99%

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estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

Woo

Lee

2009

IEEE Trans. Knowl. Data Eng.

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A sliding window‐based false‐negative approach for ubiquitous data stream analysis

Kim

Park

Kim

et al. 2011

Int J Communication

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Ubiquitous data stream mining (UDSM) is the process of performing data analysis on mobile, embedded and ubiquitous devices. In many cases, a large volume of data can be mined for interesting and relevant information in a wide variety of applications. Data stream mining requires computationally intensive mining techniques to be applied in mobile environments constrained by analysis of a real-time single pass with limited computational resources. Therefore, we have to ensure that the result is within the error tolerance range. In this paper, we suggest a method for a false-negative approach based on the Chernoff bound for efficient analysis of the data stream. Hence, we consider the problem of approximating frequency counts for space-efficient computation over data stream sliding windows. We show that a falsenegative approach allowing a controlled number of frequent itemsets to be missing from the output is a more promising solution for mining frequent itemsets from a ubiquitous data stream. These are simple to implement, and have provable quality, space, and time guarantees. The experimental results have shown that the proposed algorithms achieve a high accuracy of at least 99% and require a small execution time.

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Intelligent route generation: discovery and search of correlation between shared resources

Yen

Huang

et al. 2012

Int J Communication

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Sharing information and resources on the Internet has become an important activity for education. The use of ubiquitous devices makes it possible for learning participants to be engaged in an open and connected social environment, and also allows the learning activities to be performed at any time and any place. In this study, the discovery of correlation among shared resources is concentrated. A hypothetical scenario is considered that the information, such as photos and thoughts, is applicable to be shared with implicit context (i.e., geographical information) by learners through a practical implementation, PadSCORM, on a mobile device. Two major contributions are achieved. First, the correlations among resources are determined through usage experiences mining and geographical information adjustment. It then assists learners in filtering out redundant information by highlighting the significance of resources. Second, an intelligent searching algorithm is proposed to visualize adaptive routes to facilitate search process and to enrich the learning activity. The empirical experiments revealing the feasibility and performance (e.g., accuracy and effectiveness) are conducted in the universities in North Taiwan.Social network analysis concentrates on the discovery of implicit correlation via the explicit exchange of resources among participants. The participant, including the human user and/or object,

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Finding recent frequent itemsets adaptively over online data streams

Cited by 96 publications

References 0 publications

estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

A sliding window‐based false‐negative approach for ubiquitous data stream analysis

Intelligent route generation: discovery and search of correlation between shared resources

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