scholarly journals A Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

2021 ◽  
Vol 1 (4) ◽  
pp. 9-15
Author(s):  
S. Rana ◽  
M. N. I. Mondal
Keyword(s):  
Association Rules ◽  
Multilevel Data ◽  
Buying Behavior ◽  
Market Basket Analysis ◽  
Association Rules Mining ◽  
Market Basket ◽  
Strategic Plans ◽  
Frequent Itemsets Mining ◽  
Retail Marketing ◽  
The Relationship

Market Basket Analysis is an observational data mining methodology to investigate the consumer buying behavior patterns in retail Supermarket. It analyzes customer baskets and explores the relationship among products that helps retailers to design store layouts, make various strategic plans and other merchandising decisions that have a big impact on retail marketing and sales. Frequent itemsets mining is the first step for market basket analysis. The association rules mining uncovers the relationship among products by looking what products the customers frequently purchase together. In retail marketing, the transactional database consists of many itemsets that are frequent only in a particular season however not taken into consideration as frequent in general. In some cases, association rules mining at lower data level with uniform support doesn't reflect any significant pattern however there is valuable information hiding behind it. To overcome those problems, we propose a methodology for mining seasonally frequent patterns and association rules with multilevel data environments. Our main contribution is to discover the hidden seasonal itemsets and extract the seasonal associations among products in additionally with the traditional strong regular rules in transactional database that shows the superiority for making season based merchandising decisions. The dataset has been generated from the transaction slips in large supermarket of Bangladesh that discover 442 more seasonal patterns as well as 1032 seasonal association rules in additionally with the regular rules for 0.1% minimum support and 50% minimum confidence.

A new approach for association rules mining using computational and artificial intelligence

2020 ◽  
Vol 39 (5) ◽  
pp. 7233-7246
Author(s):  
Fahed Yoseph ◽  
Markku Heikkilä
Keyword(s):  
Artificial Intelligence ◽  
Association Rules ◽  
Market Basket Analysis ◽  
Holistic View ◽  
Association Rules Mining ◽  
Point Of Sale ◽  
Market Basket ◽  
Market Intelligence ◽  
Product Assortment ◽  
Analysis Process

Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.

Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

2021 ◽  
Vol 2 (1) ◽  
pp. 132-139
Author(s):  
Wiwit Pura Nurmayanti ◽  
Hanipar Mahyulis Sastriana ◽  
Abdul Rahim ◽  
Muhammad Gazali ◽  
Ristu Haiban Hirzi ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Pattern Growth ◽  
Product Sales ◽  
The Relationship

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.  

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

scholarly journals Genotyping of Digital Music Subscribers

2018 ◽  
Author(s):  
Rafael Vargas
Keyword(s):  
Association Rules ◽  
Direct Marketing ◽  
Digital Music ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Subjective Perspective ◽  
Input Variables

This paper presents a methodology to categorize subscribers of digital music service (DMS) by taking as input variables their historic download pattern and streaming library. Drawing inspiration from biology, we develop a metric called "genotype" by defining a series of indicators called attractors and detractors that form a category space or "species" for every user. These species are based on four main styles of music: latin, urban, rock and pop; the indicators assign weights to the genres based on the sociological subjective perspective of music fans from one category in relation to other music styles, i.e., how they view other types of music they don't feel affinity with. The result is a segmentation of users that finds application in the making of offers and promotions, which can in turn be coupled with association rules and market basket analysis to improve direct marketing campaigns (CTR) and maximize revenue.

scholarly journals Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad

2019 ◽  
Vol 13 (4) ◽  
pp. 323
Author(s):  
Eferoni Ndruru ◽  
Taronisokhi Zebua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Hiding ◽  
Text Message ◽  
Third Party ◽  
Least Significant Bit ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Sales Data ◽  
The Third

Stenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods.

scholarly journals Market Basket Analysis in a Financial Institution

2019 ◽  
Vol 1 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Felipe Rezende ◽  
Marcelo Ladeira
Keyword(s):  
Association Rules ◽  
Financial Institution ◽  
Sao Paulo ◽  
The State ◽  
Data Preparation ◽  
Market Basket Analysis ◽  
Market Basket ◽  
The Subject ◽  
Future Work ◽  
The Way

This article demonstrates a study on Market Basket Analysis of a financial institution, showing rules of personal consumer association of the state of São Paulo. A concept about three association algorithms is presented, but a study with only one is performed. The paper is divided into an introduction, describing a brief account of the reason for choosing the subject. Understanding the business, where it is explained about the financial institution and the importance of the study to the institution. The way the data are handled is demonstrated in Understanding the Data, just as the Data Preparation is described in the sequence, putting all the filters and treatments that were done on the data. In the following, it is described the Modeling, which reports on algorithms of association rules and on examples of these algorithms, as well as which algorithm was chosen to be treated in the paper. Evaluation explains on the results obtained with the study and the Implementation as it was done all the analysis of the data and the results obtained. Finally, we have the Conclusion about the learning obtained with the article and what future work to do. 

scholarly journals Urban association rules: Uncovering linked trips for shopping behavior

2016 ◽  
Vol 45 (2) ◽  
pp. 367-385 ◽  
Author(s):  
Yuji Yoshimura ◽  
Stanislav Sobolevsky ◽  
Juan N Bautista Hobin ◽  
Carlo Ratti ◽  
Josep Blat
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Shopping Behavior ◽  
Edge Weight ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Shopping Experience ◽  
Bank Card ◽  
The City

In this article, we introduce the method of urban association rules and its uses for extracting frequently appearing combinations of stores that are visited together to characterize shoppers’ behaviors. The Apriori algorithm is used to extract the association rules (i.e. if -> result) from customer transaction datasets in a market-basket analysis. An application to our large-scale and anonymized bank card transaction dataset enables us to output linked trips for shopping all over the city: the method enables us to predict the other shops most likely to be visited by a customer given a particular shop that was already visited as an input. In addition, our methodology can consider all transaction activities conducted by customers for a whole city. This approach enables us to uncover not only simple linked trips such as transition movements between stores but also the edge weight for each linked trip in the specific district. Thus, the proposed methodology can complement conventional research methods. Enhancing understanding of people’s shopping behaviors could be useful for city authorities and urban practitioners for effective urban management. The results also help individual retailers to rearrange their services by accommodating the needs of their customers’ habits to enhance their shopping experience.

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