Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

Tang, Huijun; Wang, Le; Liu, Yangguang; Qian, Jiangbo

doi:10.1155/2022/5379086

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Since the first FIM algorithm, Apriori [2], many such algorithms have been presented in the last two decades. These algorithms are classified into different types, such as sequential patterns mining algorithms [24,25], data stream mining algorithms [26,27], graph mining algorithms [28,29], approximate frequent itemset mining in uncertain data [30,31], and high utility frequent itemset mining algorithms [32,33]. In this section, we present the FIM algorithms relevant to the research presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

Sadeequllah,

Rauf,

Rehman

et al. 2024

IEEE Access

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Frequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transactional data is memory-intensive. The FIM is even more resource-hungry for dense data than for sparse data. The rapidly growing size of datasets further exacerbates this situation and necessitates the design of out-of-the-box highly efficient solutions. This paper proposes a novel approach to frequent itemset mining for dense datasets. This approach, after the initial stage, does not use transactional data, which makes it memory efficient. It also replaces processing-intensive support calculations with efficient support predictions, which are probabilistic and need no transactional data. To predict the support of an itemset, it only needs the support of its subsets. However, this technique works only for itemsets of size three or higher. We also propose an FIM algorithm ProbBF, which incorporates this technique. The ProbBF discards transactional data after it uses it to calculate frequent one and two-size itemsets. For the itemsets of size 𝑘, where 𝑘 ≥ 3, ProbBF uses the proposed probabilistic technique to predict their support. It is considered frequent if the predicted support is greater than a given threshold. Our experiments show that ProbBF is efficient in both time and space against state-of-the-art FIM algorithms that use transactional data. The experiments also show that ProbBF can successfully generate the majority of the frequent itemsets on real-world datasets. Since ProbBF is probabilistic, some loss in quality is inevitable.

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