Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

scholarly journals Analisis Tingkat Pembelian Konsumen dengan Algoritma Apriori

2020 ◽  
Vol 2 (02) ◽  
Author(s):  
Ismasari Ismasari ◽  
Maulida Ramadhan ◽  
Wahyu Hadikristanto
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Minimum Support ◽  
Mining Association Rule

Saat ini data mining telah diimplementasikan ke berbagai bidang salah satu diantaranya adalah pada bidang bisnis atau perdagangan yang dapat membantu para pebisnis dalam kebijakan pengambilan keputusan terhadap apa yang berhubungan dengan persediaan barang. Misalnya pentingnya sistem persediaan barang di suatu Toko dan jenis barang apa yang menjadi prioritas utama yang harus di stok untuk mengantisipasi kekosongan barang. Karena minimnya stok barang dapat berpengaruh pada pelayanan konsumen dan pendapatan Toko. Metode yang sering digunakan untuk menganalisa pola pembelian pelanggan adalah metode asosiasi atau association rule mining. Association rule mining adalah suatu metode untuk mencari pola hubungan antar satu atau lebih itemset yang ada dalam suatu dataset. Algoritma yang paling popular dalam mencari pola hubungan item set adalah algoritma apriori atau sering disebut dengan market basket analysis. Proses yang dilakukan dalam penelitian ini menggunakan tools Rapid Miner untuk mengolah data dengan algoritma apriori, dari pengujian yang dilakukan dengan parameter yang telah ditentukan yaitu minimum support 70% dan minimum confidence 80% menghasilkan 4 aturan asosiasi dengan nilai confidance 100% yaitu kombinasi item aqua 600ml-fulloblasto caramel cruncy chocolat - yupi 500 semua rasa - beng beng 25g. Dengan pencarian pola menggunakan algoritma apriori ini diharapkan informasi yang dihasilkan dapat meningkatakan strategi penjualan selanjutnya    

scholarly journals Association Rule Mining for Intrusion Detection System: A Survey

2019 ◽  
Vol 8 (1) ◽  
pp. 20-24
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Detection System ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Detection Systems ◽  
Large Sets

Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

scholarly journals Mining predicate rules without minimum support threshold

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Hafiz I. Ahmad ◽  
◽  
Alex T. H. Sim ◽  
Roliana Ibrahim ◽  
Mohammad Abrar ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Predicate Logic ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Minimum Support ◽  
Support Threshold ◽  
Definition Of

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

scholarly journals Algoritma Apriori untuk Pencarian Frequent itemset dalam Association Rule Mining

2019 ◽  
Vol 7 (2) ◽  
pp. 143-152
Author(s):  
Lusa Indah Prahartiwi ◽  
Wulan Dari
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemset ◽  
Data Availability ◽  
Department Stores ◽  
Frozen Foods ◽  
Rule Mining ◽  
Snack Foods ◽  
Transactional Data

Abstract   Over decades, retail chains and department stores have been selling their products without using the transactional data generated by their sales as a source of knowledge. Abundant data availability, the need for information (or knowledge) as a support for decision making to create business solutions, and infrastructure support in the field of information technology are the embryos of the birth of data mining technology. Association rule mining is a data mining method used to extract useful patterns between data items. In this research, the Apriori algorithm was applied to find frequent itemset in association rule mining. Data processing using Tanagra tools. The dataset used was the Supermarket dataset consisting of 12 attributes and 108.131 transaction. The experimental results obtained by association rules or rules from the combination of item-sets beer wine spirit-frozen foods and snack foods as a Frequent itemset with a support value of 15.489% and a confidence value of 83.719%. Lift ratio value obtained was 2.47766 which means that there were some benefits from the association rule or rules.   Keywords: Apriori, Association Rule Mining.   Abstrak   Selama beberapa dekade rantai ritel dan department store telah menjual produk mereka tanpa menggunakan data transaksional yang dihasilkan oleh penjualan mereka sebagai sumber pengetahuan. Ketersediaan data yang melimpah, kebutuhan akan informasi (atau pengetahuan) sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan cikal-bakal dari lahirnya teknologi data mining. Data mining menemukan pola yang menarik dari database seperti association rule, correlations, sequences, classifier dan masih banyak lagi yang mana association rule adalah salah satu masalah yang paling popular. Association rule mining merupakan metode data mining yang digunakan untuk mengekstrasi pola yang bermanfaat di antara data barang. Pada penelitian ini diterapkan algoritma Apriori untuk pencarian frequent itemset dalam association rule mining. Pengolahan data menggunakan tools Tanagra. Dataset yang digunakan adalah dataset Supermarket yang terdiri dari 12 atribut dan 108.131 transaksi. Hasil eksperimen diperoleh aturan asosiasi atau rules dari kombinasi itemsets beer wine spirit-frozen foods dan snack foods sebagai Frequent itemset dengan nilai support sebesar 15,489% dan nilai confidence sebesar 83,719%. Nilai Lift ratio yang diperoleh sebesar 2,47766 yang artinya terdapat manfaat dari aturan asosiasi atau rules tersebut.   Kata kunci: Apriori, Association rule mining  

scholarly journals Implying Association Rule Mining and Market Basket Analysis for Knowing Consumer Behavior and Buying Pattern in Lockdown - A Data Mining Approach

2021 ◽  
Author(s):  
Anurag Sinha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Indian Market ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Innovation Development ◽  
Market Basket ◽  
Data Mining Approach ◽  
Profound Knowledge ◽  
Online Stores

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

2016 ◽  
Vol 23 ◽  
pp. 120-130
Author(s):  
Manoj Kumar ◽  
Hemant Kumar Soni
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Business Decisions ◽  
Depth Analysis ◽  
Intelligent Tools

Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

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