MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques

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

Survey performance Improvement FP-Tree Based Algorithms Analysis

Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. For large databases, the research on improving the mining performance and precision is necessary; so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally the entire frequent item sets discovery from the database in the process of association rule mining shares of larger, these algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori .the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm

Early Detection and Prevention of Oral Cancer: Association Rule Mining on Investigations

Abstract: -Early detection and prevention of oral cancer is critical, as it can increase the survival chances considerably, allow for simpler treatment and result in a better quality of life for survivors. In this research paper, the popular association rule mining algorithm, apriori is used to find the spread of cancer with the help of various investigations and then assess the chance of survival of the patient. This is achieved by extracting a set of significant rules among various laboratory tests and investigations like FNAC of neck node, LFT, Biopsy, USG, CT scan-MRI and survivability of the oral cancer patients. The rules clearly show that if FNAC of neck node, USG and CT scan/ MRI is positive then chance of survival is reduced. However, if LFT is normal, probability of survival is high. If diagnostic-biopsy results in squamous-cell-carcinoma then it clearly indicate oral cancer, which may lead to high mortality if appropriate treatment is not initiated. The experimental results demonstrate that all the generated rules hold the highest confidence level, thereby, making investigations very essential to understand the spread of cancer after clinical examination for early detection and prevention of oral cancer

Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good quality measure remains a challenging task for a user. Given a quality measures evaluation matrix according to semantic properties, this paper describes how FCA can highlight quality measures with similar behavior in order to help the user during his choice. The aim of this article is the discovery of Interestingness Measures "IM" clusters, able to validate those found due to the hierarchical and partitioning clustering methods "AHC" and "k-means". Then, based on the theoretical study of sixty one interestingness measures according to nineteen properties, proposed in a recent study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure