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 IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)

2020 ◽  
Vol 10 (2) ◽  
pp. 138
Author(s):  
Muhammad SyahruRomadhon ◽  
Achmad Kodar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Tourist Attractions ◽  
Market Basket ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Minimum Support

Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.  

MODEL SISTEM INFORMASI PEMASARAN SEBAGAI PENUNJANG KEPUTUSAN PROMOSI PRODUK MENGGUNAKAN ALGORITMA MAX -MINER

ICIT Journal ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 94-104
Author(s):  
Fernando Siboro ◽  
Capri Eriansyah ◽  
Muhammad Adi Sofyan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Market Basket Analysis ◽  
Market Basket

Teknologi informasi saat ini terus berkembang semakin cepat, membuat pola berfikir manusia berubah, dengan proses pertumbuhan yang seperti ini, generasi akan datang diharuskan mempunyai keahlian yang lebih baik di bidang pemanfaatan teknologi informasi. Kebutuhan adanya kemudahan dari segi pemasaran, saat ini dirasa sangat penting, terutama bagi perusahaan yang bergerak dibidang penjulan atau distributor guna menunjang meningkatkan akurasi dan kualitas pemasaran itu sendiri. Namun pada kenyataanya, sistem yang berjalan masih tergolong kurang efektif dan efesien dalam melayani kebutuhan pelanggan, hal ini dikarenakan sistem pemasaran produk hanya bisa diakses secara manual, dan belum adanya media informasi seputar produk yang ditawarkan, oleh sebab itu dibuatlah suatu perancangan sistem informasi yang mengatur pemasaran produk dan dapat menjadi bahan dalam pembuatan laporan sistem penunjang keputusan. Dalam perancangan ini menggunakan metode data mining market basket analysis dan Max-Miner sebagai algoritma. Serta menggunakan metode penerapan sistem waterfall atau sering dinamakan siklus hidup klasik (classic life cycle). Dengan demikian rancang bangun sistem informasi ini, mengacu kepada bagaimana cara agar pemasaran produk dapat di akses dengan mudah, cepat, dan akurat dimanapun dan kapanpun, calon customer dapat mengakses tanpa terkendala waktu dan tempat, serta menjadi wadah dalam pengambilan keputusan oleh perusahaan. Metodologi desain menggunakan uml yang melimuti usecase, activity, squence dan untuk pengelolaan basis data menggunakan mysql. Sistem ini diharapkan mampu dijadikan salah satu penunjang keputusan untuk kebutuhan promosi produk. Kata Kunci: Penunjang pemasaran, promosi produk, algoritma Max-Miner

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.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests

2012 ◽  
pp. 91-114
Author(s):  
Stanislav Kreuzer ◽  
Natascha Hoebel
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Ordinal Data ◽  
Customer Relationships ◽  
User Profiles ◽  
Data Mining Techniques

One of the keys to building effective e-customer relationships is an understanding of consumer behavior online. However, analyzing the behavior of customers online is not necessarily an indicator of their interests. Therefore, building profiles of registered users of a website is of importance if it goes beyond collecting obvious information the user is willing to give at the time of the registration. These user profiles can contribute to the analysis of the users’ interests. Important tools for the analysis are data-mining techniques, for example, the clustering of collected user information. This chapter addresses the problem of how to define, calculate, and visualize fuzzy clusters of Web visitors with respect to their behavior and supposed interests. This chapter shows how to cluster Web users based on their profile and by their similar interests in several topics using the fuzzy and hybrid CORD (Clustering of Ordinal Data) clustering system, which is part of the Gugubarra Framework.

Concept Drift

2011 ◽  
pp. 202-206 ◽  
Author(s):  
Marcus A. Maloof
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
User Interfaces ◽  
Concept Drift ◽  
Market Basket Analysis ◽  
Intelligent User Interfaces ◽  
Market Basket ◽  
The Subject ◽  
Traditional Approaches ◽  
E Mail

Traditional approaches to data mining are based on an assumption that the process that generated or is generating a data stream is static. Although this assumption holds for many applications, it does not hold for many others. Consider systems that build models for identifying important e-mail. Through interaction with and feedback from a user, such a system might determine that particular e-mail addresses and certain words of the subject are useful for predicting the importance of e-mail. However, when the user or the persons sending e-mail start other projects or take on additional responsibilities, what constitutes important e-mail will change. That is, the concept of important e-mail will change or drift. Such a system must be able to adapt its model or concept description in response to this change. Coping with or tracking concept drift is important for other applications, such as market-basket analysis, intrusion detection, and intelligent user interfaces, to name a few.

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