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.

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 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.

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.

scholarly journals Optimization of Store Layout using Market Basket Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 6459-6463
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Product Placement ◽  
Retail Store ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Arduous Task ◽  
History Of ◽  
Shopping Basket ◽  
Grid Layout

Store layout is a crucial factor for attracting customers in a retail store. Use of appropriate store layout results in an increase in sales of the store. Grid layout, free flow layout, spine layout is a few commonly used store layouts in the retail store. The grid layout is used for supermarkets but the placement of different products as per the preference of the customer is quite an arduous task there. Purchase history of a supermarket can be utilized to predict the preferences of the customers and can be utilized as an aid for designing a better store layout. Market basket analysis is employed to get insights from the POS data of the supermarket. Market basket analysis (MBA) helps to extract the various association rules from the purchase data of the shoppers. A customer can pick different items identified with the items that the person has just put in his or her shopping basket or cart which frames an association rule. The extraction of such rules can help in the appropriate product placement in the store as per the shopper’s preference.

scholarly journals MARKET BASKET ANALYSIS (MBA) PADA SITUS WEB E-COMMERCE ZAKIYAH COLLECTION

2016 ◽  
Vol 7 (2) ◽  
pp. 459
Author(s):  
Ari Muzakir ◽  
Laili Adha
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Product Delivery ◽  
Customer Profiles

E-commerce menghubungkan antara produsen dengan produsen, produsen dengan konsumen, konsumen dengan produsen, konsumen dengan konsumen. Untuk mengimplementasi e-commerce dalam mendukung bisnis organisasi perlu di perhatikan 5 komponen utama yaitu ; pengembangan produk, promosi, transaksi online, product delivery dan after sales support. Hal ini yang tengah diterapkan pada Zakiyah Collection. Zakiyah Collection bergerak dibidang penjualan aneka macam kain khas Palembang seperti songket, blongket,tanjung, dan lain sebagainya. Untuk melakukan analisis terhadap pangsa pasar yang ada agar dapat bersaing dengan toko online lainnya dilakukan dengan strategi pemasaran dengan menggunakan pendekatan market basket analysis (MBA). MBA merupakan salah satu teknik dari data mining yang digunakan untuk menentukan produk-produk manakah yang akan dibeli oleh pelanggan secara bersamaan dengan melakukan analisa terhadap daftar transaksi pelanggan. Dengan mengetahui produk-produk tersebut, maka sebuah sistem e-commerce dapat membuat maupun mengembangkan sebuah sistem customer profiles dan dapat menentukan layout katalog pelanggannya sendiri. Model pengembangan sistem yang dilakukan menggunakan prototype dimana pelanggan dan pengguna akan dilibatkan secara langsung dalam proses ini. Hasil akhir dalam penelitian ini adalah berupa analisis data transaksi menggunakan market basket analysis dengan dilakukan 4 kali kombinasi produk yang berdasarkan nilai support x confidence terbesar dengan hasil berupa angka-angka kemungkinan transasksi yang berkaitan dengan produk yang dijual. Jika dengan menggunakan 1 kali kombinasi, maka didapatkan blongket dengan nilai support sebesar 0.5625. Jika dilakukan 2 kali kombinasi diperoleh kombinasi blongket dan songket dengan nilai support 0.375. Kata kunci: e-commerce, market basket analysis, association rules.

scholarly journals MARKET BASKET ANALYSIS USING THE FP-GROWTH ALGORITHM TO DETERMINE CROSS-SELLING

2021 ◽  
Vol 7 (4) ◽  
pp. 49-54
Author(s):  
Fildzah Zia Ghassani ◽  
Asep Jamaludin ◽  
Agung Susilo Yuda Irawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Basic Needs ◽  
Market Basket Analysis ◽  
Rice Cake ◽  
Market Basket ◽  
Transaction Data ◽  
Minimum Support ◽  
A Value ◽  
Association Method

KAOCHEM Sinergi Mandiri Cooperative is a cooperative that provides various kinds of basic needs such as basic foodstuffs that can meet the needs of its members. The cooperative transaction data is only stored as a report. Association rules are a method in data mining that functions to identify items that have a value that is likely to appear simultaneously with other items. One implementation of the association method is Market Basket Analysis. The data used are transaction data for November 2019. Data mining is one of the processes or stages of the KDD method. The data mining process is carried out using the FP-Growth algorithm, which is one of the algorithms for calculating the sets that often appear from data. Researchers analyzed transaction data using the Rapid Miner Studio tools. In the data mining process using FP-Growth the researcher determines a minimum support value of 3% and a minimum confidence of 50%. The association process using these values ​​produces 3 strong rules, namely if ades 350 ml, then fried / lontong with a support value of 0.030 and confidence 0.556 and if fried st, then fried / lontong with a support value of 0.048 and confidence 0.639, and if nasi uduk / bacang , then fried / rice cake with a support value of 0.031 and confidence 0.824. The results of the association rules can be applied using one of the marketing techniques, namely cross-selling to increase the sales of the cooperative.

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