SAT‐based and CP‐based declarative approaches for Top‐Rank‐
            <i>K</i>
             closed frequent itemset mining

Abed, Sa’ed; Abdelaal, Areej; Alshayeji, Mohammad H.; Ahmad, Imtiaz

doi:10.1002/int.22294

Cited by 7 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frequent itemsets are patterns that occur with a frequency greater than or equal to a user‐defined threshold in a dataset (Abed et al, 2021; Deng, 2013; Han et al, 2007). For instance, a frequent itemset is a collection of items, such as bread and cheese, that frequently appear together in a transaction dataset.…”

Section: Methodsmentioning

confidence: 99%

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

2022

View full text Add to dashboard Cite

Discovering previously unknown anomalies that are rare and dramatically differ from the majority of the data is a critical need for the automotive industry. Rare itemset mining (RIM), one of the pattern‐based methods, has been used for anomaly detection due to providing successful analysis results. However, several aspects still need to be explored, such as improving the mining process by identifying more targeted, valuable and reliable rare itemsets. Motivated by this fact, this study proposes a novel approach, named ensemble of rare itemset mining (ERIM), which investigates weak rare itemsets (WRIs) using different algorithms and aggregates these rules to obtain strong rare itemsets (SRIs). This study also combines four different RIM algorithms (Apriori Rare, Apriori Inverse, CORI and RP‐Growth) as base learners for the first time. The proposed ERIM approach is a general methodology that can be applied to any field, but, in this study, it was used in the automotive industry as a case study. In the experiments, ERIM was applied to a real‐world gear manufacturing dataset to discover anomalies in machine downtimes. The experimental results were evaluated in terms of the number of itemsets and the length of itemsets by giving some samples, as well. The results showed that the proposed ERIM approach gives more reliable common knowledge by jointly considering the relation between WRIs discovered by the base learners. The findings indicated that the proposed ERIM technique was successful in detecting anomalies whose support values are below 7.12. Furthermore, it is clear from the experimental results that the ERIM discovered the highest number of SRIs, 1403, each of which is a 3‐itemset. Finally, the results showed that our method performed 43.37% better on average than state‐of‐the‐art methods on the same dataset.

show abstract

Section: Methodsmentioning

confidence: 99%

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

2022

View full text Add to dashboard Cite

show abstract

“…The method proposed here removes tuples that get updated from the SlideTree thereby avoiding the need to visit the entire previous slide. To handle the top rank k-closed frequent pattern mining problem Abed et al [17] presented two approaches. The first was based on Boolean satisfiability where an efficient algorithm was designed to improve the encoding of the problem.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Poovan

Acharya

Reddy

2023

IJECE

View full text Add to dashboard Cite

Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.

show abstract

“…7 Association rule mining, [8][9][10] which is a branch of data mining, finds the relationship between items in a database. Frequent pattern mining, [11][12][13] which is a kind of association rule mining, finds frequent patterns that are sets of items in a binary database. Apriori 14 and FP-Growth 15 are representative pattern mining algorithms in regard to a binary database.…”

Section: Introductionmentioning

confidence: 99%

Average utility driven data analytics on damped windows for intelligent systems with data streams

et al. 2021

View full text Add to dashboard Cite

In industrial areas, most of databases are dynamic databases, and the volume of the databases has grown with the passage of time. Especially, pattern mining for incremental database needs different approaches from static database because the profit or the accuracy of the previously inserted data can be reduced. Since data is How to cite this article: Kim J, Yun U, Kim H, et al. Average utility driven data analytics on damped windows for intelligent systems with data streams.

show abstract

SAT‐based and CP‐based declarative approaches for Top‐Rank‐ K closed frequent itemset mining

Cited by 7 publications

References 28 publications

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Average utility driven data analytics on damped windows for intelligent systems with data streams

Contact Info

Product

Resources

About