scholarly journals An Apriori Algorithm-Based Association Rule Analysis to Identify Acupoint Combinations for Treating Diabetic Gastroparesis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ping-Hsun Lu ◽  
Jui-Lin Keng ◽  
Fu-Ming Tsai ◽  
Po-Hsuan Lu ◽  
Chan-Yen Kuo
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Pattern ◽  
Diabetic Gastroparesis ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Potential Association ◽  
Pattern Growth ◽  
Core Elements ◽  
Rule Analysis

We explored the potential association rules within acupoints in treating diabetic gastroparesis (DGP) using Apriori algorithm complemented with another partition-based algorithm, a frequent pattern growth algorithm. Apriori algorithm is a data mining-based analysis that is widely applied in various fields, such as business and medicine, to mine frequent patterns in datasets. To search for effective acupoint combinations in the treatment of DGP, we implemented Apriori algorithm to investigate the association rules of acupoints among 17 randomized controlled trials (RCTs). The acupoints were extracted from the 17 included RCTs. In total, 29 distinct acupoints were observed in the RCTs. The top 10 frequently selected acupoints were CV12, ST36, PC6, ST25, BL21, BL20, BL23, SP6, BL18, and ST21. The frequency pattern of acupoints achieved by using a frequent pattern growth algorithm also confirms the result. The results showed that the most associated rules were {BL23, BL18} ≥ {SP6}, {BL20, BL18} ≥ {PC6}, {PC6, BL18} ≥ {BL20}, and {SP6, BL18} ≥ {BL23} in the database. Acupoints, including BL23, BL18, SP6, BL20, and PC6, can be deemed as core elements of acupoint combinations for treating DGP.

Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

2021 ◽  
Vol 2 (1) ◽  
pp. 132-139
Author(s):  
Wiwit Pura Nurmayanti ◽  
Hanipar Mahyulis Sastriana ◽  
Abdul Rahim ◽  
Muhammad Gazali ◽  
Ristu Haiban Hirzi ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Pattern Growth ◽  
Product Sales ◽  
The Relationship

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.  

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

scholarly journals FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3091
Author(s):  
Hong-Jun Jang ◽  
Yeongwook Yang ◽  
Ji Su Park ◽  
Byoungwook Kim
Keyword(s):  
Association Rules ◽  
Spatial Data ◽  
Association Rule ◽  
Clustering Algorithm ◽  
Frequent Pattern ◽  
Pattern Growth ◽  
Position Data ◽  
New Association ◽  
Iot Devices ◽  
Mining Algorithms

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

scholarly journals Combination of Acupoints in Treating Patients with Chronic Obstructive Pulmonary Disease: An Apriori Algorithm-Based Association Rule Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Po-Chun Hsieh ◽  
Chu-Fang Cheng ◽  
Chih-Wei Wu ◽  
I-Shiang Tzeng ◽  
Chan-Yen Kuo ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Association Rules ◽  
Association Rule ◽  
Binary Data ◽  
Chronic Obstructive ◽  
Apriori Algorithm ◽  
Obstructive Pulmonary Disease ◽  
Potential Core ◽  
Rule Analysis

Chronic obstructive pulmonary disease (COPD) is highly prevalent and a major burden on the healthcare system worldwide. It has a severe impact on patients due to poor health-related quality of life (HRQL), dyspnea, and exertional intolerance. Our previous meta-analysis revealed that body acupuncture therapy had adjuvant benefits of improving HRQL in COPD patients undergoing optimal medical treatment. Previous studies indicated that treatment with combinations of acupoints was more effective than single acupoint treatment. The association rule analysis has been widely used to explore relationships in acupoint combination. Therefore, we aimed to investigate the potential core acupoint combination in COPD treatment by mining the association rules from the retrieved randomized control trials (RCTs) of the previous meta-analyses. This study was conducted based on Apriori algorithm-based association rule analysis, which is a popular data mining method available in software R. We extracted acupoints as binary data from the 12 included RCTs for analysis. There were 27 acupoints extracted from 12 RCTs. The top 10 frequently selected acupoints were BL12, BL13, BL20, BL23, BL43, CV17, EXB1, LU5, LU7, and ST36. We investigated 2444 association rules, and the results showed that {ST36, BL12} ≥ {CV17}, {ST36, BL12} ≥ {EXB1}, {CV17, BL12} ≥ {ST36}, and {EXB1, BL12} ≥ {ST36} were the most associated rules in the retrieved RCTs. The acupoint combinations of ST36, BL12, and CV17 and ST36, BL12, and EXB1 could be considered as the core of acupoint combination for further acupuncture treatment of COPD.

scholarly journals An Apriori Algorithm-Based Association Rule Analysis to Identify Herb Combinations for Treating Uremic Pruritus Using Chinese Herbal Bath Therapy

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ping-Hsun Lu ◽  
Jui-Lin Keng ◽  
Ko-Li Kuo ◽  
Yu-Fang Wang ◽  
Yu-Chih Tai ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Meta Analysis ◽  
Apriori Algorithm ◽  
Potential Core ◽  
Uremic Pruritus ◽  
Chinese Herbal ◽  
Increased Risk ◽  
Rule Analysis ◽  
Bath Therapy

Uremic pruritus (UP) is prevalent among patients with end-stage renal disease (ESRD), which causes severe itching and affects their quality of life. Additionally, patients experience fatigue and depression, and an increased risk of mortality has also been reported. A meta-analysis of 17 randomized controlled trials (RCTs) has indicated that Chinese herbal bath therapy (CHBT) had adjuvant benefits in improving UP in ESRD patients, and previous studies have reported that herb combinations were more useful than treatment with a single herb. Association rule analysis has been used to evaluate potential correlations between herb combinations, and Apriori algorithms are one of the most powerful machine-learning algorithms available for identifying associations within databases. Therefore, we used the Apriori algorithm to analyze association rules of potential core herb combinations for use in CHBT for UP treatment using data from a meta-analysis of 17 RCTs that used CHBT for UP treatment. Data on 43 CHBT herbs were extracted from 17 RCTs included for analysis and we found 19 association rules. The results indicated that the following herb combinations {Chuanxiong, Baijili} ≥ {Dahuang} and {Dahuang, Baijili} ≥ {Chuanxiong} were most strongly associated, implying that these herb combinations represent potential CHBT treatments for UP.

Application of Association Rule in Students’ Results in University

2011 ◽  
Vol 181-182 ◽  
pp. 172-176
Author(s):  
Li Juan Zhou ◽  
Shuang Li ◽  
Tong Liu
Keyword(s):  
College Students ◽  
Association Rules ◽  
Association Rule ◽  
Data Preprocessing ◽  
Data Transformation ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Potential Association ◽  
Classification Category

With the improved Apriori algorithm mine the potential association rules among courses from a large number of college students’ results. First is data preprocessing, which includes course options, students (transaction) options, results classification, category statistics and Data transformation. Then carry the experiment and analyze the experimental results in detail. At last, get association rule guiding to further teaching.

scholarly journals Penerapan Data Mining Apriori Pada Persediaan Obat (Studi Kasus Apotek Rafif Farma Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 115
Author(s):  
Mardiah Mardiah
Keyword(s):  
Data Mining ◽  
Phase Analysis ◽  
Customer Service ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Inventory Systems ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Analysis Algorithm

<span><em>The importance of inventory systems at a pharmacy and the type of goods which</em><br /><span><em>are a top priority that must be in stock. It is useful to anticipate the void stuff. Due to the</em><br /><span><em>lack of inventory may affect customer service and asset to the pharmacy. Therefore, this</em><br /><span><em>study was conducted to help resolve those problems by designing a data mining</em><br /><span><em>application that serves to predict sales of the drug is needed most knowable a priori</em><br /><span><em>algorithm with the help of Tools Tanagra. One of the interesting association analysis</em><br /><span><em>phase analysis algorithm that generates a high frequency patterns (frequent pattern</em><br /><span><em>mining).</em><br /><span><em>Keywords: Data Mining, Apriori Algorithm, Association Rule</em></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

scholarly journals Marketing Strategy Using Frequent Pattern Growth

2021 ◽  
Vol 3 (2) ◽  
pp. 194-201
Author(s):  
Nazori Suhandi ◽  
Rendra Gustriansyah
Keyword(s):  
Association Rules ◽  
Marketing Strategies ◽  
Frequent Pattern ◽  
High Positive Correlation ◽  
Pattern Growth ◽  
Growth Method ◽  
Mining Methods ◽  
The Right ◽  
The Impact

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.

scholarly journals Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1107
Author(s):  
Siti Nurlela ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Automotive Industry ◽  
Association Rule ◽  
The Other ◽  
Apriori Algorithm ◽  
Sales Data ◽  
Competitive Prices ◽  
Rule Analysis ◽  
Strategy Pattern

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

Review of Association Mining Methods for the Extraction of Rules Based on the Frequency and Utility Factors

2021 ◽  
Vol 12 (4) ◽  
pp. 1-10
Author(s):  
Subba Reddy Meruva ◽  
Venkateswarlu Bondu
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Strong Association ◽  
Vital Role ◽  
Frequent Pattern ◽  
Association Mining ◽  
Observation Study ◽  
Mining Methods ◽  
High Utility ◽  
High Utility Itemsets

Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Applications like e-commerce, marketing, healthcare, and web recommendations, etc. consist of items with their utility or profit. Such cases, utility-based itemsets mining methods, are playing a vital role in the generation of effective association rules and are also useful in the mining of high utility itemsets. This paper presents the survey on high-utility itemsets mining methods and discusses the observation study of existing methods with their experimental study using benchmarked datasets.

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