TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

Yasir, Muhammad; Habib, Muhammad Asif; Ashraf, Muhammad; Sarwar, S.; Chaudhry, Muhammad Umar; Shahwani, Hamayoun; Faisal, C. M. Nadeem

doi:10.1109/access.2019.2959878

Cited by 5 publications

(7 citation statements)

References 56 publications

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“…TRICE algorithm optimizes HARPP by generating iterative TLs too; however, TRICE prunes the database first by discarding the infrequent itemsets from every distinct transaction [53]. Therefore, it represents Iterative Trimmed Transaction Lattices (ITTLs) by generating power sets of pruned transactions, where each ITTL is a tiny subset of IL.…”

Section: Related Workmentioning

confidence: 99%

“…Optimized RElim and SaM also perform well on sparse datasets [54]. D-Gene is implemented in Python and the implementation of TRICE is taken from the authors' previous work [53]. RElim, SaM, and FP-Growth are obtained from [85].…”

Section: A Empirical Settingsmentioning

confidence: 99%

“…Techniques based on Iterative Transaction Lattice (ITL) are recently proposed to learn frequent itemsets from large real datasets that are sparse too [52], [53]. Sparse real-world transactional datasets contain numerous similar transactions.…”

Section: Introductionmentioning

confidence: 99%

“…Sparse real-world transactional datasets contain numerous similar transactions. Based on the Iterative Trimmed Transaction Lattice (ITTL), the TRICE algorithm efficiently explores frequent itemsets from sparse real datasets [53]. For large datasets, trimmed versions of two or more distinct transactions become similar.…”

Section: Introductionmentioning

confidence: 99%

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D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

et al. 2020

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Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.INDEX TERMS Big data applications, pattern recognition, association rules, frequent item set mining, IoT.

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Section: Related Workmentioning

confidence: 99%

Section: A Empirical Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

et al. 2020

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“…Frequent itemset mining is the first and foremost step of association rule mining [6]. In association rules mining and Frequent itemset mining literature, frequent Itemset mining methods are mainly divided into two main categories: 1) algorithms that mine frequent itemset that takes advantage of the horizontal data format.…”

Section: Related Workmentioning

confidence: 99%

A Fast Approach for Up-Scaling Frequent Itemsets

2020

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With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem. However, the relationships among datasets of different scales are not fully utilized. A fast approach proposed in this paper is as follows: the frequent itemsets on the large-scale data are directly inferred based on the frequent itemsets that are belonged small-scale datasets, instead of mined from the large-scale dataset again on condition that the frequent itemsets on the small-scale datasets have been mined. We conduct extensive experiments on one synthetic data and four UCI data sets. The experimental results show that our algorithm is significantly faster and consumes less memory than these leading algorithms. INDEX TERMS Up-scaling, up-scaling frequent itemsets, frequent itemset mining, data mining.

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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

et al. 2023

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Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.INDEX TERMS Columnar databases, stable periodic-frequent itemset, itemset mining. I. INTRODUCTIONDatabase systems play a crucial role in storing the big data generated by real-world applications. Depending on the layout used for storing the data, one can broadly classify the databases into two types: row databases and columnar databases. 1 Row databases are primarily based on ACID 2 properties and organize the data as records by keeping the data associated with a record next to each other in a storage device. The popular row databases include MySQL [1] and Postgres [2]. In contrast, columnar databases are based on BASE 3 properties and organize data into fields and storeThe associate editor coordinating the review of this manuscript and approving it for publication was Laura Celentano .1 Columnar and row databases are referred as vertical and horizontal databases, respectively.2 ACID is an acronym for Atomicity, Consistency, Isolation, and Duration.3 BASE is an acronym for Basically Available, Soft state, and Eventually consistent.

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TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

Cited by 5 publications

References 56 publications

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

A Fast Approach for Up-Scaling Frequent Itemsets

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

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