Mining High Utility Itemsets with Regular Occurrence

Amphawan, Komate; Lenca, Philippe; Jitpattanakul, Anuchit; Surarerks, Athasit

doi:10.5614/itbj.ict.res.appl.2016.10.2.5

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…The regularity concept is also introduced in mining high-utility itemsets. An efficient single-scan algorithm called MHUIRA, is the contribution of Amphawan [27]. This algorithm finds regularly co-occurring items with high utility values.…”

Section: ) Related Work In Static/stream Datasetmentioning

confidence: 99%

Finding Partial Periodic and Rare Periodic Patterns in Temporal Databases

Upadhya,

Paleja,

Geetha

et al. 2023

IEEE Access

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Most of the periodic pattern mining algorithms extract fully periodic patterns by strictly monitoring the cyclic behaviour of patterns in transactional as well as temporal databases. The most recent and preferred method for discarding non-periodic uninteresting patterns is partial periodic pattern mining, which has control over the strictness measure on cyclic repetitions of patterns. Recently, a variety of industries, including fraud detection, telecommunications, retail marketing, research, and medical have found applications for rare association rule mining, which uncovers unusual or unexpected combinations. A limited amount of literature demonstrated how periodicity is essential in mining low-support patterns. However, time of occurrence is also a vital phrase that is ignored which further aids in significant information retrieval. With this inspiration, a novel depth-first search framework named 3P-BitVectorMiner, is proposed to extract entire partial periodic patterns from a temporal database. Experiments are carried out by varying support and periodicity thresholds for a variety of datasets. It is found that 3P-BitVectorMiner consistently displays greater performance over the state-of-the-art algorithm 3P-Growth. Further, the scalability of the 3P-BitVectorMiner algorithm is also presented to demonstrate the efficiency over the 3P-Growth algorithm on large temporal databases. In addition, two variations named RFPP-BitVectorMiner and R3P-BitVectorMiner are proposed to mine rare fully periodic patterns and rare partial periodic patterns from temporal databases respectively. Different experiments carried out show that these proposed frameworks successfully capture periodic uncommon patterns in temporal databases.

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Section: ) Related Work In Static/stream Datasetmentioning

confidence: 99%

Finding Partial Periodic and Rare Periodic Patterns in Temporal Databases

Upadhya,

Paleja,

Geetha

et al. 2023

IEEE Access

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“…To overcome the problem, HUSPM which maintains the important sequence of items is proposed [18-20, 37, 38]. Regular occurrence of items in HUIM [34] is also interesting to investigate. It can be used to investigate the occurrence behavior of itemsets with their utility values.…”

Section: Other Variationsmentioning

confidence: 99%

“…To solve that problem, many research have been proposed such as Apriori based [7,8], tree based [9][10][11][12][13][14][15][16][17], utility list based [18-23, 26-31, 33, 34, 36-38] and hybrid based algorithm [24,25]. Moreover, there are several HUIM variations such as high average utility itemset mining (HAUIM) [16,35], HUIM in incremental databases [17,32], HUIM in sequential database (HUSPM) [18-20, 37, 38], HUIM in regular occurrence [34], Close HUIM [29,31,33], and correlated HUIM [28,30]. These kinds of HUIM are introduced to make the results more meaningful.…”

Section: Introductionmentioning

confidence: 99%

A Review of High Utility Itemset Mining for Transactional Database

Atmaja

Sonawane²

2022

Lecture Notes in Electrical Engineering

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High utility itemset mining (HUIM) is an expansion of frequent itemset mining (FIM). Both of them are techniques to find interesting patterns from the database. The interesting patterns found by FIM is based on frequently appeared items. This approach is not that efficient to identify the desired patterns, as it considers only existence or non existence of items in database and ignores utility. Whereas the patterns are more meaningful for the user if the utility is considered. The utility can be quantity, profit, cost, risk or other factors based on user interest. HUIM is another approach to find interesting patterns by considering utility of items along with the frequency. It uses minimum utility threshold to determine if an itemset is high utility itemset (HUI) or not. There are several challenges to implement utility from traditional pattern mining to HUIM. Lately, there are many research contributions that proposed different algorithms to solve these issues. This review work explores various HUIM techniques with detailed analysis of different strategies like Apriori, Tree based, Utility Lists based, and Hybrid. These strategies are used to implement various HUIM techniques in order to achieve the effectiveness in pattern mining. The observations and analytical findings based on this detailed review done with respect to various parameters can be recommended and used for further research in the pattern mining.

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Mining High-Utility Irregular Itemsets

Laoviboon

Amphawan

2019

Studies in Big Data

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Mining High Utility Itemsets with Regular Occurrence

Cited by 8 publications

References 34 publications

Finding Partial Periodic and Rare Periodic Patterns in Temporal Databases

Finding Partial Periodic and Rare Periodic Patterns in Temporal Databases

A Review of High Utility Itemset Mining for Transactional Database

Mining High-Utility Irregular Itemsets

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