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

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Online retailers and merchants increasingly request feedback from their clients on the products they purchase. This has led to a significant increase in the number of product reviews posted online, as more people are making purchases online. The opinions expressed in these customer reviews have a significant impact on other customers' purchase decisions, as they are influenced by other customers' recommendations or complaints. This study used Amazon, a well-known and widely used e-commerce platform, to examine sentiment categorization using several machine learning techniques while analyzing an Amazon Reviews dataset. At first, the reviews were transformed into vector representations using the Bag-of-Words approach. Word cloud was used to illustrate the text data in terms of the frequency they appear in the review. Subsequently, the machine learning methods decision trees and logistic regression were used. The two models used in this study achieved high levels of accuracy in analyzing the dataset. Specifically, the Decision Tree model outperformed the Logistic Regression one, achieving an impressive accuracy of 99% compared to the 94% of the latter

A BINARY-BASED APPROACH FOR MINING ASSOCIATION RULES IN LARGE-SCALE DATABASE ENVIRONMENTS

Kajian ini mempertimbangkan penyelesaian masalah corak kerap dan kaedahkaedah perlombongan peraturan perkaitan ("association rules mining") di dalam pangkalan data yang berskala besar. Masalah ini adalah didorong oleh hakikat bahawa jumlah data telah berganda setiap tahun; namun dari segi lain, teknik-teknik untuk mengekstrakkan kegunaan data ini nampaknya bergerak pada kadar yang jauh lebih perlahan. Walaupun alatan bagi teknik-teknik tradisional statistik dan pengurusan data sudah wujud; tetapi mereka tidak lagi mencukupi untuk menganalisa kerumitan data yang berskala besar. Begitu juga dengan alatau yang biasa digunakan oleh sistem sokongan keputusan; mereka tidak berkemampuan untuk melakukan analisis prospektif secara automatik. Oleh itu, teknik perlombongan data telah muncul beberapa tahun kebelakangan ini bagi menyelesaikan masalah tersebut

A BINARY-BASED APPROACH FOR MINING ASSOCIATION RULES IN LARGE-SCALE DATABASE ENVIRONMENTS

Kajian ini mempertimbangkan penyelesaian masalah corak kerap dan kaedahkaedah perlombongan peraturan perkaitan ("association rules mining") di dalam pangkalan data yang berskala besar. Masalah ini adalah didorong oleh hakikat bahawa jumlah data telah berganda setiap tahun; namun dari segi lain, teknik-teknik untuk mengekstrakkan kegunaan data ini nampaknya bergerak pada kadar yang jauh lebih perlahan. Walaupun alatan bagi teknik-teknik tradisional statistik dan pengurusan data sudah wujud; tetapi mereka tidak lagi mencukupi untuk menganalisa kerumitan data yang berskala besar. Begitu juga dengan alatau yang biasa digunakan oleh sistem sokongan keputusan; mereka tidak berkemampuan untuk melakukan analisis prospektif secara automatik. Oleh itu, teknik perlombongan data telah muncul beberapa tahun kebelakangan ini bagi menyelesaikan masalah tersebut

Analyzing Customer Satisfaction Over Telecom Companies in Sudan Using Apriori Algorithm

With the rapid growth in the field of information and communication technology, the demand for telecommunications and internet services increased over the years. In Sudan, three reputed telecom companies provide these types of services. Although a great competition between these companies in order to attract more new customers and also to keep the existing customers satisfied, many new and existing customers still has some apprehensions over the difficulty on how to choose the best among the service providers in terms of full range of services with affordable cost. This study aims to measure the customer satisfaction from the telecom companies in Sudan. The survey questionnaire designed and distributed electronically to as many customers to find frequent patterns by applying association rule mining techniques. The results of analysis show that Zain Sudan performs better compared to MTN and Sudani telecom companies in terms of various customer-oriented services