Market Basket Analysis: Case Study of a Supermarket

2020 ◽  
pp. 727-734
Author(s):  
Anup R. Pillai ◽  
Dhananjay A. Jolhe
Keyword(s):  
Market Basket Analysis ◽  
Market Basket

Market Basket Analysis: A New Tool in Ecology to Describe Chemical Relations in the Environment—A Case Study of the Fern Athyrium distentifolium in the Tatra National Park in Poland

2010 ◽  
Vol 36 (9) ◽  
pp. 1029-1034 ◽  
Author(s):  
Aleksandra Samecka-Cymerman ◽  
Andrzej Stankiewicz ◽  
Krzysztof Kolon ◽  
Alexander J. Kempers ◽  
Rob S. E. W. Leuven
Keyword(s):  
National Park ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Tatra National Park

scholarly journals Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

2021 ◽  
Vol 4 (2) ◽  
pp. 383-392
Author(s):  
Firmansyah Firmansyah ◽  
Agus Yulianto
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Sales Promotion ◽  
Frequent Pattern ◽  
Analysis Method ◽  
Analysis Framework ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Industry Standard

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

scholarly journals Geographic data-mining and extraction of association rules using the Apriori algorithm (Case study: Capital of Iranian provinces)

2021 ◽  
Author(s):  
Farimah Houshmand-Nanehkaran ◽  
Seyed Mohammadreza Lajevardi ◽  
Mahmoud Mahlouji-Bidgholi
Keyword(s):  
Association Rules ◽  
Decision Making Process ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Temporal Complexity ◽  
Geographic Data Mining ◽  
Urban Characteristics ◽  
Degree Of Similarity

Abstract Extracting of association rules between urban features provides latent and considerable information for urban planners about the relationships between urban characteristics and their similarities. For this purpose, in this paper, the most famous and well-known Apriori algorithm is used. We present the Fariori algorithm to delay the characteristics that can be deleted during execution, as well as to achieve main and frequent features in the early stages with efficient changes to the Apriori algorithm. Although the spatial and temporal complexity of both algorithms is exponential based on the number of fea-tures, in practice, by implementing the Fariori algorithm in MATLAB, we achieved more rules than the existing software (R, Weka, Market Basket Analysis and, Yarpiz). In the proposed algorithm, it is possible to determine the degree of similarity by adjust-ing the support and confidence ratio parameters to identify a coherent set of similar cities. The used database includes cities of 31 in the provincial capitals of Iran. Dis-covering the association rules leads to similar cities finding and can be an efficient aid in the decision-making process.

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

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