The coffee shop business offers a diverse range of and food options. However, customers often experience delays during transactions due to the extensive selection menu items combinations. This inconvenience not only discomforts new but also hampers their likelihood returning, potentially impacting overall turnover. To address this issue, study aims establish association rules by combining least...

Frequent itemset mining plays an important part in college library data analysis. Because there are a lot of redundant data in library database, the mining process may generate intra-property frequent itemsets, and this hinders its efficiency significantly. To address this issue, we propose an improved FP-Growth algorithm we call RFP-Growth to avoid generating intra-property frequent itemsets, ...

We proposed a new algorithm SuffixMiner which eliminates the requirement of multiple passes through the data when finding out all frequent itemsets in data streams, takes full advantage of the special property of suffixtree to avoid generating candidate itemsets and traversing each suffix-tree during the itemset growth, and utilizes a new itemset growth method to mine all frequent itemsets in d...

Sequential Pattern Mining (SPM) is one of the key areas in Web Usage Mining (WUM) with broad applications such as analyzing customer behavior from weblog files. The current algorithms in this area can be classified into two broad areas, namely, apriori-based and pattern-growth based. Apriori based algorithms for mining sequential patterns need to scan the database many times as they focus on ca...

This paper proposes an algorithm for generating irreducible cubic trinomials in the form x + ax + b, b ∈ Fp, where a is a certain fixed non-zero element in the prime field Fp. The proposed algorithm needs a certain irreducible cubic trinomial over Fp to be previously given as a generator; however, the proposed algorithm can generate irreducible cubic polynomials one after another by changing a ...

The research proposes a planning scenario of critical success factors (CSFs) "conceptual-framework" for enterprise systems (ES) of Smart aircraft factory. Application of knowledge discovery and classification algorithms is applied to draw probabilistic inferences based on FP-Growth-algorithm blended with a novel approach based on "balanced scorecard (BSC) architecture".

In this paper, we propose a new version of FP-Growth algorithm to find association rules. In this version, we vary the minsup value from one level to another. This variation is made in two cases: increasing and decreasing the minsup value. We performed a set of experiments to validate the usefulness of our proposition in the generation of association rules process.