An efficient algorithm for mining temporal high utility itemsets from data streams

Chu, Chun-Jung; Tseng, Vincent S.; Liang, Tyne

doi:10.1016/j.jss.2007.07.026

Cited by 86 publications

(46 citation statements)

References 11 publications

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“…Traditional association rules mining models assume that the utility of each item is always 1 and the sales quantity is either 0 or 1, thus it is only a special case of utility mining, where the utility or the sales quantity of each item could be any number. Chu et al proposed THUI-Mine that can identify the temporal high utility itemsets by generating fewer temporal high transaction-weighted utilization 2-itemsets in data streams [8]. Giannella et al developed a FP-tree-based algorithm, FP-stream, to mine frequent itemsets at multiple time granularities by a novel titled-time windows technique [9].…”

Section: Related Workmentioning

confidence: 99%

“…A Mushroom database has been used extensively in the AI area. In this experiment, we compare only the relative performance of the THUI-Mine [8], DSM-FI [5] and WSFI-Mine. Figures 7 and 8 show the execution times for the three algorithms on dataset T10I4D100K and the Mushroom database, respectively, as the minimum support threshold is increased from 0.2% to 1%.…”

Section: Performance Comparisonmentioning

confidence: 99%

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Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Kim¹,

Kim²,

Kim³

2010

Journal of Information Processing Systems

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Abstract-A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Kim¹,

Kim²,

Kim³

2010

Journal of Information Processing Systems

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“…Chu, Tseng, & Liang [6] first proposed a method, named THUI-Mine algorithm, for mining temporal high utility itemsets from data streams. THUI-Mine divides the database into several partitions, and used the filtering threshold and the database reduction method to reduce the number of candidate itemsets.…”

Section: Introductionmentioning

confidence: 99%

Mining High Utility Itemsets in Data Streams Based on the Weighted Sliding Window Model

Tsai¹

2014

IJDKP

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“…To address these issues, utility mining [2,5,6,7,8,11,13,15,19,20,24,26] emerges as an important topic in data mining. In utility mining, each item has a weight (e.g.…”

Section: Introductionmentioning

confidence: 99%

Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets

Fournier-Viger

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-Mining high utility itemsets from transactional databases is an important data mining task, which refers to the discovery of itemsets with high utilities (e.g. high profits). Although several studies have been carried out, current methods may present too many high utility itemsets for users, which degrades the performance of the mining task in terms of execution time and memory requirement. To achieve high efficiency for the mining task and provide a concise mining result to users, we propose a novel framework in this paper for mining closed + high utility itemsets, which serves as a compact and lossless representation of high utility itemsets. We present an efficient algorithm called CHUD (Closed + High utility itemset Discovery) for mining closed + high utility itemsets. Further, a method called DAHU (Derive All High Utility itemsets) is proposed to recover all high utility itemsets from the set of closed + high utility itemsets without accessing the original database. Results of experiments on real and synthetic datasets show that CHUD and DAHU are very efficient and that our approach achieves a massive reduction in the number of high utility itemsets (up to 800 times in our experiments). In addition, when all high utility itemsets can be recovered by DAHU, the combination of CHUD and DAHU outperforms the state-of-the-art algorithms for mining high utility itemsets.

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An efficient algorithm for mining temporal high utility itemsets from data streams

Cited by 86 publications

References 11 publications

Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Mining High Utility Itemsets in Data Streams Based on the Weighted Sliding Window Model

Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets

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