Mining top-K high utility itemsets

Wu, Cheng; Shie, Bai-En; Tseng, Vincent S.; Yu, Philip S.

doi:10.1145/2339530.2339546

Cited by 107 publications

(71 citation statements)

References 19 publications

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“…The different top-k concepts are included in references [2,5,8,9,10].In top-k no need of threshold. Top-k values are retrieved.…”

Section: Literature Surveymentioning

confidence: 99%

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

Augustin¹,

Paul²,

Nair³

2017

IJCA

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High utility itemset mining is an uncommon term. But we are using it while we are doing online purchases etc. It is a part of business analytics. Its main application area is market basket analysis where when a customer purchases an item he can buy another item to maximize profit. So both the customer and business vendors earn profit. This one is not a new concept and is derived from frequent itemset mining. Here we proposes an algorithm for mining closed high utility itemset using top-k algorithm. So that execution time will be less and space efficiency can also be achieved. Both the concept of closed high utility itemset and top-k mining are existing. The new concept is that integrating the merits of them together. The algorithm used for closed hui mining is CHUD.Similarly the algorithm used for top-k mining is TKU,TKO etc. Also recovering all HUIs from complete set of CHUIs using DAHU algorithm.

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“…The different top-k concepts are included in references [2,5,8,9,10].In top-k no need of threshold. Top-k values are retrieved.…”

Section: Literature Surveymentioning

confidence: 99%

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

Augustin¹,

Paul²,

Nair³

2017

IJCA

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“…Negative unit profits of items are addressed in [15,16]. To avoid difficulties in setting a proper utility threshold, in [17][18][19] attempts were done to mine a set of itemsets with the highest utility. Podpecan [20] and Sugunadevi [21] proposed to discover high utility-frequent itemsets based on consideration of utility and frequency of occurrence.…”

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

“…The remaining items in 1 are then ordered by ≻. Next, the utility and remaining utility of each item in 1 are calculated (line [13][14][15][16][17][18]. If the utility of item is not smaller than the utility threshold, MHUIRA identifies item as 1-HUIR and then collects item into HUIR.…”

Section: -Huir Identificationmentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

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Abstract. High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.

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“…This is called closed frequent patterns. Moreover, some issues focus on the Top-K mining problem [13,14,31] since the Top-K frequent patterns are more useful than other frequent patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Zhu et al [13] proposed an efficient algorithm Spider-Mine to mine the Top-K largest frequent patterns from a single massive network with any user-specified probability. Wu et al [14] proposed a novel framework for mining the Top-K high-utility itemsets. Classical sequential pattern mining algorithms include GSP [4] and Prefix Span [15].…”

Section: Introductionmentioning

confidence: 99%

Mining sequential patterns with periodic wildcard gaps

Wang

Ren

et al. 2014

Appl Intell

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Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specified threshold, and a variable gap-length with wildcards between each two consecutive letters. The task is to gain all frequent patterns with periodic wildcard gaps. State-of-the-art mining algorithms which use matrix or other linear data structures to solve the problem not only consume a large amount of memory but also run slowly. In this study, we use an Incomplete Nettree structure (the last layer of a Nettree which is an extension of a tree) of a sub-pattern P to efficiently create Incomplete Nettrees of all its super-patterns with prefix pattern P and compute the numbers of their supports in a one-way scan. We propose two new algorithms, MAPB (Mining sequentiAl Pattern using incomplete Nettree with Breadth first search) and MAPD (Mining sequentiAl Pattern using incomplete Nettree with Depth first search), to solve the problem effectively with low memory requirements. Furthermore, we design a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top-K frequent patterns for each length. Experimental results on real-world biological data demonstrate the superiority of the proposed algorithms in running time and space consumption and also show that the pattern matching approach can be employed to mine special frequent patterns effectively.

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Mining top-K high utility itemsets

Cited by 107 publications

References 19 publications

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

Mining High Utility Itemsets with Regular Occurrence

Mining sequential patterns with periodic wildcard gaps

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