Discovering Periodic-Frequent Patterns in Transactional Databases

Tanbeer, Syed Khairuzzaman; Ahmed, Chowdhury Farhan; Jeong, Byeongmoon; Lee, Young-Koo

doi:10.1007/978-3-642-01307-2_24

Cited by 150 publications

(77 citation statements)

References 9 publications

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“…However, these algorithms are not designed to discover Proceedings of the 2nd Czech-China Scientific Conference 2016periodic patterns. Inspired by the work on FIM, researchers have designed several algorithms to discover periodic frequent patterns (PFP) in transaction databases [4][5][6][7][8][9]. Several applications of mining periodic frequent patterns have been reported in previous work [9].…”

Section: Related Workmentioning

confidence: 99%

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PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Fournier‐Viger¹,

Lin²,

Duong³

et al. 2017

Proceedings of the 2nd Czech-China Scientific Conference 2016

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Periodic pattern mining is the task of discovering patterns that periodically appear in transactions. Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user. A major drawback of this approach is that it is not flexible, as a pattern can be discarded based on only one of its periods. In this chapter, we present a solution to this issue by proposing to discover periodic patterns using three measures: the minimum periodicity, the maximum periodicity, and the average periodicity. The combination of these measures has the advantage of being more flexible. Properties of these measures are studied. Moreover, an efficient algorithm named PFPM (Periodic Frequent Pattern Miner) is proposed to discover all frequent periodic patterns using these measures. An experimental evaluation on real data sets shows that the proposed PFPM algorithm is efficient and can filter a huge number of nonperiodic patterns to reveal only the desired periodic patterns.

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Section: Related Workmentioning

confidence: 99%

“…Several algorithms have been proposed to discover periodic frequent patterns (PFP) [4][5][6][7][8][9] in a transaction database (a sequence of transactions). Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user.…”

Section: Introductionmentioning

confidence: 99%

PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Fournier‐Viger¹,

Lin²,

Duong³

et al. 2017

Proceedings of the 2nd Czech-China Scientific Conference 2016

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“…Tanbeer [3] has proposed the problem of frequent-regular itemset mining (FRIM) in order to observe occurrence behavior of itemsets in terms of frequency and regularity of occurrence. A set of itemsets with high frequency and regular occurrence that meets user-given support and regular thresholds is generated.…”

Section: Frequent-regular Itemset Mining (Frim)mentioning

confidence: 99%

“…Lastly, the remaining utility in each entry of NUL, total remaining utility, and total utility of each item are calculated (as shown in Figure 1 15 , 8,11,39,9 ). Since regularity is greater than (itemset ' ' does not regularly occur in the database), ' ' should be removed out of consideration (based on the downward closure property of regularity [3]). Next, item ' ' is merged with item ' ' and is intersected with 1,8,65,0 , 3,4,30,0 , 4,5,5,0 , 5, 1,12, 0 , 8,4 Since regularity is smaller than and is greater than , it can be concluded that itemset ' ' and its supersets are potentially HUIRs.…”

Section: Example Of Mhuira With Numentioning

confidence: 99%

“…In addition, frequent itemset mining only considers the frequency of occurrence as a criterion to measure interestingness of itemsets, which may not be sufficient to track interesting occurrence behavior of itemsets. Thus, Tanbeer, et al [3] proposed to add a regularity constraint to discover sets of items that frequently and regularly occur in a collection of data. This can help to find out about the regularity of product purchase, which can in turn help to manage inventory, design a marketing strategy, etc.…”

Section: Introductionmentioning

confidence: 99%

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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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A survey of itemset mining

Fournier‐Viger

Lin

et al. 2017

WIREs Data Min & Knowl

193

137

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Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

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Discovering Periodic-Frequent Patterns in Transactional Databases

Cited by 150 publications

References 9 publications

PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Mining High Utility Itemsets with Regular Occurrence

A survey of itemset mining

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