Mining the Weighted Frequent XML Query Pattern

Gu, Mi Sug; Hwang, Jeong Hee; Chai, Duck Jin; Ryu, Keun Ho

doi:10.1109/iwsca.2008.37

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…A normalization process is required to adjust the differences among data to generate a common basis for the comparison, because the real values are too various. Because the weight condition is a useful factor that can extensively be employed in various areas, weight‐based research has actively been performed in a variety of relevant pattern mining approaches and applications (Tao, ; Wang et al ., ; Gu & Hwang, ; Tsai, ; Lee et al ., 2015; Lee et al ., 2015). WFIM (Yun, ) is an algorithm for mining frequent patterns considering weight conditions.…”

Section: Related Workmentioning

confidence: 99%

“…As another approach for selectively mining, a smaller number of patterns considered as more important ones by users, weighted frequent pattern (WFP) mining approaches have been proposed (Tao, ; Wang et al ., ; Gu & Hwang, ; Tsai, ), which can consider different priority for each item. Because not all of the extracted frequent patterns are useful, it is important to consider weight conditions of items or patterns additionally into the mining process.…”

Section: Introductionmentioning

confidence: 99%

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Efficient representative pattern mining based on weight and maximality conditions

Yun

Lee

2016

Expert Systems

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As a core area in data mining, frequent pattern (or itemset) mining has been studied for a long time. Weighted frequent pattern mining prunes unimportant patterns and maximal frequent pattern mining discovers compact frequent patterns. These approaches contribute to improving mining performance by reducing the search space. However, we need to consider both the downward closure property and patterns' subset checking process when integrating these different methods in order to prevent unintended pattern losses. Moreover, it is also essential to extract valid patterns with faster runtime and less memory consumption. For this reason, in this paper, we propose more efficient maximal weighted frequent pattern (MWFP) mining approaches based on tree and array structures. We describe how to handle these problems more efficiently, maintaining the correctness of our method. We develop two types of maximal weighted frequent mining algorithms based on weight ascending order and support descending order and compare these two algorithms to conclude which is more suitable for MWFP mining. In addition, comprehensive tests in this paper show that our algorithms are more efficient and scalable than state‐of‐the‐art algorithms, and they also have the correctness of the MWFP mining in terms of their pattern generation results.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient representative pattern mining based on weight and maximality conditions

Yun

Lee

2016

Expert Systems

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“…The main limitation of the above algorithm is that they are based on the Apriori algorithm [1] that requires all candidate generation and examining with multiple scans of the original transaction database. Thus, weighted frequent pattern mining algorithms based on the pattern growth method have been proposed [2,8,12,22,27,33,34]. Moreover, lots of mining methods applied in various areas have been studied, such as mining weighted sequential patterns [5,32,35], weighted patterns over data streams [3,24,28], approximate weighted patterns [36,37], etc.…”

Section: Weighted Frequent Pattern Miningmentioning

confidence: 99%

Efficient mining of maximal correlated weight frequent patterns

Yun

Ryu

2013

IDA

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Maximal frequent pattern mining has been suggested for data mining to avoid generating a huge set of frequent patterns. Conversely, weighted frequent pattern mining has been proposed to discover important frequent patterns by considering the weighted support. We propose two mining algorithms of maximal correlated weight frequent pattern (MCWP), termed MCWP(WA) (based on Weight Ascending order) and MCWP(SD) (based on Support Descending order), to mine a compact and meaningful set of frequent patterns. MCWP(SD) obtains an advantage in conditional database access, but may not obtain the highest weighted item of the conditional database to mine highly correlated weight frequent patterns. Thus, we suggest a technique that uses additional conditions to prune lowly correlated weight items before the subsets checking process. Analyses show that our algorithms are efficient and scalable.

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“…The proper determination of weight reflects both the objective information of the alarm and the subjective judgment of experts. More extensions with weight constraints have been developed, such as mining weighted association rules [31], mining weighted association rules without pre-assigned weights [30], mining weighted sequential patterns [41,42], mining weighted closed patterns [36], mining frequent patterns with dynamic weights [2], mining weighted graphs [24], mining weighted sub-trees or sub-structures [25], and mining weighted frequent XML query patterns [15]. However, in the above applications, weighted frequent pattern mining discovers important patterns and maximal frequent pattern mining extracts fewer patterns by compressing the frequency information.…”

Section: Introductionmentioning

confidence: 99%