A Log File Analysis Technique Using Binary-Based Approach

Fageeri, Sallam Osman; Ahmad, Rohiza; Baharum, Baharum

doi:10.1007/978-981-4585-18-7_1

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Nowadays, it has been used in many different fields, such as health care [4][5][6], decision support system [5], telecommunication networks [7], crime investigation [8], Intrusion detection [9], and log file analysis [10]. Mining association rule is commonly used in data mining, i.e.…”

Section: Introductionmentioning

confidence: 99%

MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques

Fageeri

Kausar

Soosaimanickam

2023

IJRITCC

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This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out to handle the computational cost for discovering the frequent itemsets, but it still needs more exploration and developments. In this paper, we introduce an efficient Bitwise-Based data structure technique for mining frequent pattern in large-scale databases. The algorithm scans the original database once, using the Bitwise-Based data representations as well as vertical database layout, compared to the well-known Apriori and FP-Growth algorithm. Bitwise-Based technique enhance the problems of multiple passes over the original database, hence, minimizes the execution time. Extensive experiments have been carried out to validate our technique, which outperform Apriori, Éclat, FP-growth, and H-mine in terms of execution time for Market Basket Analysis.

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