Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

2019 ◽  
pp. 518-536 ◽  
Author(s):  
Sugam Sharma
Keyword(s):  
Data Mining ◽  
Physical Exercise ◽  
Human Health ◽  
Chronic Diseases ◽  
Association Rule ◽  
Health Informatics ◽  
Daily Activity ◽  
Market Basket Analysis ◽  
Market Basket

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individual's health.

Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

2017 ◽  
Vol 7 (2) ◽  
pp. 1-18
Author(s):  
Sugam Sharma
Keyword(s):  
Data Mining ◽  
Physical Exercise ◽  
Human Health ◽  
Chronic Diseases ◽  
Association Rule ◽  
Health Informatics ◽  
Daily Activity ◽  
Market Basket Analysis ◽  
Market Basket

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individual's health.

scholarly journals Aplikasi Data Mining dalam penentuan layout swalayan dengan menggunakan metode MBA

2020 ◽  
Vol 5 (2) ◽  
pp. 130
Author(s):  
Muhammad Rizki ◽  
Desi Devrika ◽  
Isnaini Hadiyul Umam ◽  
Fitriani Surayya Lubis
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Pattern Growth ◽  
Item Item

Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci:  Data mining, MBA, Association rule, pattern growth, layout                         Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci:  Data mining, MBA, Association rule, pattern growth, layout

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

scholarly journals Association Rule Mining for Intrusion Detection System: A Survey

2019 ◽  
Vol 8 (1) ◽  
pp. 20-24
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Detection System ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Detection Systems ◽  
Large Sets

Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

scholarly journals PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MINING

2012 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Altin J Rindengan
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Fuzzy Set ◽  
Association Rule ◽  
The Other ◽  
Binary Form ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Good Relationship

PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MININGABSTRAKSalah satu analisis dalam data mining adalah market basket analysis untuk menganalisa kecenderungan pembelian suatu barang yang berasosiasi dengan barang yang lain. Dalam tulisan ini membahas aturan asosiasinya dengan mempertimbangkan jumlah item barang yang dibeli dalam satu transaksi. Asumsinya adalah keterkaitan pembelian suatu barang dengan barang yang lain dalam satu transaksi akan semakin kecil jika jumlah item barang yang dibeli semakin banyak. Tulisan ini menganalisa asosisasi antar item barang dengan membuat tabel transaksi dalam bentuk nilai fuzzy set dibandingkan dengan analisa asosiasi yang biasa dilakukan dalam bentuk biner. Berdasarkan analisis terhadap data yang digunakan memberikan hasil support dan confidence yang cenderung lebih kecil tetapi lebih realistis dibanding aturan asosisasi biasa. Keywords: analisis market basket, association rule, data mining, fuzzy c-partition.COMPARISON OF ASSOCIATION RULE WITH BINARY AND FUZZY C-PARTITION FORM AT MARKET BASKET ANALYSIS ON DATA MININGABSTRACTOne analysis in data mining is market basket analysis to analyze the purchase of a good trends associated with other items. In this paper discussing the association rules by considering the number of items purchased in one transaction. The assumption is that the purchase of a good relationship with the other items in one transaction will be smaller if the number of items purchased items more and more. This paper analyzes the association between the items of goods by making the transaction table in the form of fuzzy sets of values to compare with analysis of the usual associations in binary form. Based on the analysis of the data used to support and confidence of which tend to be smaller but more realistic than usual asosisasi rules. Keywords: market basket analysis, association rule, data mining, fuzzy c-partition.

scholarly journals ASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH

2020 ◽  
Vol 7 (4) ◽  
pp. 102
Author(s):  
Delila Melati ◽  
Titi Sri Wahyuni
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Analysis Method ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Time Processing ◽  
Processing Data ◽  
Analyze Data

Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth

scholarly journals Analisa Association Rule Pada Algoritma Apriori Untuk Minat Pembelian Alat Kesehatan

2021 ◽  
Vol 5 (1) ◽  
pp. 280
Author(s):  
Andi Rahmadsyah ◽  
Hartono Hartono ◽  
Rika Rosnelly
Keyword(s):  
Data Mining ◽  
Medical Devices ◽  
Association Rule ◽  
A Priori ◽  
Mining Method ◽  
Market Basket Analysis ◽  
Business World ◽  
Market Basket ◽  
Analysis Techniques ◽  
The Relationship

In the competition in the business world, especially the Medical Device industry, it requires developers to find an accurate strategy that can increase sales of goods. One way to overcome this problem is to continue to provide various types of medical devices in the warehouse. To find out what medical devices are purchased by consumers, market basket analysis techniques are carried out, namely analysis of consumer buying habits. In order to make it easier for companies to determine Buyers' interest in medical devices, a data mining method is needed which is accompanied by an a priori algorithm based on the purchasing process carried out by consumers based on the relationship between the products purchased. Based on the sample sales data for medical devices CV Andira Karya Jaya, amounting to 25 transactions and in this study a minimum support = 12% and a minimum confidence = 70% will be used. In the final stage, the results obtained are medical devices that are in demand by buyers at CV. Andira Karya Jaya, namely 1 M3 oxygen cylinder and 1 M3 troley of oxygen. Based on this data, CV. Andira Karya Jaya can provide supplies of medical devices that are of interest to buyers.

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.  

scholarly journals Analisis Tingkat Pembelian Konsumen dengan Algoritma Apriori

2020 ◽  
Vol 2 (02) ◽  
Author(s):  
Ismasari Ismasari ◽  
Maulida Ramadhan ◽  
Wahyu Hadikristanto
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Minimum Support ◽  
Mining Association Rule

Saat ini data mining telah diimplementasikan ke berbagai bidang salah satu diantaranya adalah pada bidang bisnis atau perdagangan yang dapat membantu para pebisnis dalam kebijakan pengambilan keputusan terhadap apa yang berhubungan dengan persediaan barang. Misalnya pentingnya sistem persediaan barang di suatu Toko dan jenis barang apa yang menjadi prioritas utama yang harus di stok untuk mengantisipasi kekosongan barang. Karena minimnya stok barang dapat berpengaruh pada pelayanan konsumen dan pendapatan Toko. Metode yang sering digunakan untuk menganalisa pola pembelian pelanggan adalah metode asosiasi atau association rule mining. Association rule mining adalah suatu metode untuk mencari pola hubungan antar satu atau lebih itemset yang ada dalam suatu dataset. Algoritma yang paling popular dalam mencari pola hubungan item set adalah algoritma apriori atau sering disebut dengan market basket analysis. Proses yang dilakukan dalam penelitian ini menggunakan tools Rapid Miner untuk mengolah data dengan algoritma apriori, dari pengujian yang dilakukan dengan parameter yang telah ditentukan yaitu minimum support 70% dan minimum confidence 80% menghasilkan 4 aturan asosiasi dengan nilai confidance 100% yaitu kombinasi item aqua 600ml-fulloblasto caramel cruncy chocolat - yupi 500 semua rasa - beng beng 25g. Dengan pencarian pola menggunakan algoritma apriori ini diharapkan informasi yang dihasilkan dapat meningkatakan strategi penjualan selanjutnya    

scholarly journals Implementation of Apriori Algorithm for Determining Purchase Patterns in One Transaction

2018 ◽  
Vol 7 (4.33) ◽  
pp. 204
Author(s):  
Murnawan . ◽  
Ardiles Sinaga ◽  
Ucu Nughraha
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Future Developments ◽  
Operational Activities ◽  
Purchase Patterns

The organization data owned is one of the assets of the organization. With the daily operational activities, the longer the data will increase. By using techniques that can do data processing, these data can be obtained important information that can be used for future developments. Association rules are one of these techniques which aims to find patterns in the form of products that are often purchased together or tend to appear together in a transaction from transaction data which is generally very large by using the concept association rules themselves derived from Market Basket Analysis terminology, namely search for relationships from several products in a purchase transaction. In designing this application will build applications that classify the data items based on the tendency to appear together in a transaction using the Apriori Algorithm. The Apriori algorithm is the first algorithm and is often used to find association rules in data mining applications with association rule techniques. 

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