Fast mining erasable itemsets using NC_sets

Deng, Zhihong; Xu, Xiaoran

doi:10.1016/j.eswa.2011.09.143

Cited by 57 publications

(31 citation statements)

References 20 publications

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“…The experimental results in Deng and Lv (2014) show that FIN consumes less memory than PrePost while they have almost the same efficiency. Besides used in frequent itemset mining, itemset representation based on node list also proves to be very suitable for erasable itemset mining (Deng & Xu, 2012;Le et al, 2014) and top-rank-k frequent pattern mining (Deng, 2014;Huynh-Thi-Le, Le, Vo, & Le, 2015).…”

Section: Related Workmentioning

confidence: 99%

PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning

Deng

2015

Expert Systems with Applications

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139

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Section: Related Workmentioning

confidence: 99%

PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning

Deng

2015

Expert Systems with Applications

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139

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“…As another method, MERIT [4] has been proposed. The approach uses a tree structure, WPPC-tree, which can keep an item name, a gain, and order information into each node.…”

Section: Erasable Pattern Miningmentioning

confidence: 99%

“…To do that, they need to analyze product information such as products with components, production costs, and profits. As one of the product analysis methods, traditional erasable pattern mining approaches [4,9] consider relationships between items included in products and profits of the products. In particular, they focus on a profit for each product only.…”

Section: Motivating Examplementioning

confidence: 99%

“…Gain(X) MaxMCR (4) In the equation, MWG(X) is the gain of X devided by MaxMCR. If MWG(X) is less than MaxGain, the pattern is a candidate of a weighted erasable candidate pattern.…”

Section: Mwg(x) =mentioning

confidence: 99%

“…Table 4 shows the information such as an item name, a frequency, and total profit of products corresponding to each item. In addition, we introduce an improved tree structure that is optimized from the tree structure of the previous approaches [4,9] so that the proposed algorithm can efficiently mine weighted erasable patterns. First, Table 4 Item information for example database in Table 1 Item we difine a WEP-tree for storing item information of product configurations to conduct the pattern growth process.…”

Section: Propertymentioning

confidence: 99%

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Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Lee

Yun

Ryang

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Erasable pattern mining is one of the variations in frequent pattern mining, and its main goal is to maximize the production capacity of manufacturing industries by quickly overcoming the financial crises that can occur in industries. However, existing erasable pattern mining approaches utilize only profit information for each product, but do not consider distinct weights of items organizing the products. In addition, previous algorithms spend much time and space mining erasable patterns due to their inefficient pattern mining methods. In a real-life environment, considering weights of items in each product can be more important compared to calculating product profits only. For this reason, we propose a novel algorithm for mining weighted erasable patterns by considering the distinct weight of each item. Moreover, we discuss both discovering weighted erasable patterns and minimizing the resource availability of erasable pattern mining processes utilizing weight conditions. Especially, our approach has advantages for time and space resource consumption compared to existing approaches because our algorithm uses a pattern pruning method and stores item information included in product databases by using both compact tree and hash list structures. We present performance evaluation utilizing both real and synthetic datasets to demonstrate the efficiency of our algorithm.

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Efficiently mining erasable stream patterns for intelligent systems over uncertain data

et al. 2020

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Data mining is a method for extracting useful information that is necessary for a system from a database. As the types of data processed by the system are diversified, the transformed pattern mining techniques for processing these type of data have been proposed. Unlike the traditional pattern mining methods, erasable pattern mining is a technique for finding the patterns that can be removed by coming with a small profit. Erasable pattern mining should be able to process data by considering both the environment that the data are generated from and the characteristics of the data. An uncertain database is a database that is composed of uncertain data. Since erasable patterns discovered from uncertain data contain significant information, these patterns need to be extracted. In addition, databases gradually increase, because the data from various fields is generated and accumulated over data streams. Data streams should be processed as intelligently as possible to provide the useful data to the system in real time. In this paper, we propose an efficient erasable pattern mining algorithm that processes uncertain data that is generated over data streams. The uncertain erasable patterns discovered through the suggested technique are more meaningful information by considering the probability of the item and the profit. Moreover, the proposed method can perform efficient mining operations by using both tree and list structures. The performance of the suggested algorithm is verified through the performance tests compared with state‐of‐the‐art algorithms using real data sets and synthetic data sets.

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Fast mining erasable itemsets using NC_sets

Cited by 57 publications

References 20 publications

PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning

PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Efficiently mining erasable stream patterns for intelligent systems over uncertain data

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