An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree

Jamsheela, O.; Raju, G.

doi:10.1007/s10044-022-01111-1

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…Data mining pertains to the algorithmic exploration of vast quantities of data in an attempt to uncover concealed information [1]. The current maximum size of big data in a single data storage unit is several terabytes; this represents a rate of growth that exceeds the processing capacity of current software systems capable of capturing, storing, managing, processing, and analyzing enormous data chunks promptly [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Al-Badani,

Al-Khulaidi

2023

jast

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Frequent set-item mining is a widely used technique in the field of data mining. The algorithm utilized in Association Rules constitutes the primary source of contention; this approach is memory-intensive and time-intensive. The extraction process for concealed patterns of frequent item sets, on the other hand, becomes more time-consuming as the volume of data grows. Therefore, the necessary algorithm for mining the patterns of frequently occurring concealed item sets must be memory-efficient and fast-running. By analyzing various algorithms for locating frequent item sets, this paper aims to contribute to the development of a more efficient algorithm in this domain. The paper will evaluate the efficiency of different algorithms by considering their memory usage and runtime. Additionally, it will explore potential improvements to existing algorithms to enhance their performance in mining frequently occurring concealed item sets.

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Section: Introductionmentioning

confidence: 99%

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Al-Badani,

Al-Khulaidi

2023

jast

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IFGDS: An Interactive Fraud Groups Detection System for Medicare Claims Data

Zhan

Wang

et al. 2023

Lecture Notes in Computer Science

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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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An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree

Cited by 4 publications

References 49 publications

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

IFGDS: An Interactive Fraud Groups Detection System for Medicare Claims Data

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

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