scholarly journals Penerapan Association Rules - Market Basket Analysis untuk Mencari Frequent Itemset dengan Algoritma FP-Growth

2021 ◽  
Vol 6 (2) ◽  
pp. 61
Author(s):  
Cut Rizki Artsitella ◽  
Amrina Rosyada Apriliani ◽  
Septi Ashari
Keyword(s):  
Association Rules ◽  
Frequent Itemset ◽  
Special Method ◽  
Retail Stores ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Associative Relation ◽  
Product Layout ◽  
Main Attraction

<p><strong>In retail stores, product variations and prices are the main attraction. Products with many discounts are the most sought-after products. The promotion itself requires a special method for determining the discount. The layout in supermarkets is also something that retail stores need to pay attention to. One method that can be used to determine the product layout, promo for each product is Market Basket Analysis. The purpose of this research is to determine associative relation that occurs between items and to find out the solution to the problem of layout arrangement, catalog creation, and determination of shopping vouchers in Gading Mas Swalayan 1 based on the output of Rapid Miner software. Based on the output results obtained 7 associative relationships that have a lift ratio value &gt; 1 and it can be seen the determination of the layout of the item, catalog, and shopping voucher form. Layout changes are made for the comfort and convenience of consumers in taking the products they need and cataloging is determined by combining frequently purchased products with products that are rarely purchased. And the making of shopping vouchers is used to provide discounted prices where this is to reduce inventory and attract consumers.</strong></p><p><strong>Keywords – </strong><em>Market Basket Analysis, Rapid Miner, Retail, The relation of associative</em></p>

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 Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming

2021 ◽  
Vol 3 (2) ◽  
pp. 0210206
Author(s):  
Kelik Sussolaikah
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Market Basket Analysis ◽  
Process Data ◽  
Market Basket ◽  
Data Mining Techniques ◽  
Programming Tool ◽  
The World ◽  
R Programming

Data mining is one of the fields of science in the world of informatics which has an important role, especially with regard to data. There are many algorithms and methods that can be used to process data. The paper this time the author tries to conduct research on consumer behavior by using one of the data mining techniques, namely market basket analysis. This research uses the R Programming tool, where it is hoped that the research can be carried out effectively and efficiently. Based on the research conducted, it is known that there has been a significant purchase of several items that have been described as a plot. The tendency of consumers to buy several items followed by other items can be a consideration for arranging the layout of goods on the sales shelf or arranging product stock in a supermarket.

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. 

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