AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES

2009 ◽  
Keyword(s):  
Association Rules ◽  
Frequent Pattern ◽  
Rare Association ◽  
Pattern Growth ◽  
Growth Approach

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.

An improved frequent pattern growth method for mining association rules

2011 ◽  
Vol 38 (5) ◽  
pp. 5154-5161 ◽  
Author(s):  
Ke-Chung Lin ◽  
I-En Liao ◽  
Zhi-Sheng Chen
Keyword(s):  
Association Rules ◽  
Frequent Pattern ◽  
Pattern Growth ◽  
Growth Method ◽  
Mining Association Rules

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 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 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.

scholarly journals Sistem Informasi Penentuan Paket Pembelian Produk Menggunakan Algoritma Frequent Pattern-Growth pada KPRI Jember

2020 ◽  
Vol 8 (2) ◽  
pp. 59
Author(s):  
Mohammad Ivan Noorkholid ◽  
Muhammad Arief Hidayat ◽  
Gama Wisnu Fajarianto
Keyword(s):  
Association Rules ◽  
Frequent Pattern ◽  
Minimum Support ◽  
Pattern Growth

Penelitian ini bertujuan untuk merancang dan membangun sistem informasi penentuan paket pembelian produk pada KPRI Jember. Sistem ini menggunakan algoritma Fp-Growth (Frequent Pattern Growth) untuk menghasilkan informasi tentang paket pembelian produk dengan menangkap fenomena yang terjadi dalam transaksi penjualan. Implementasi algoritma Frequent Pattern Growth menggunakan PHP. Data hasil perhitungan algoritma Frequent Pattern Growth divisualisasikan dalam halaman website. Penerapan algoritma Frequent Pattern Growth didukung dengan metode association rules untuk menghasilkan data yang lebih lengkap dan akurat. Perhitungan membutuhkan data masukan berupa minimum support dan minimum confidence untuk memproses data transaksi penjualan menjadi paket pembelian produk. Dengan memasukkan minimum support 1 dan minimum confidence sebesar 50, hasil perhitungan didapatkan bahwa paket pembelian produk yang muncul sebanyak 10 paket dengan minimum support yang bermacam-macam berurutan dari produk pertama sampai produk terakhir. Semakin besar minimum support dan minimum confidence yang dimasukkan maka semakin sedikit paket pembelian produk yang dihasilkan.

scholarly journals Research of Improved FP-Growth Algorithm in Association Rules Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yi Zeng ◽  
Shiqun Yin ◽  
Jiangyue Liu ◽  
Miao Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Experimental Results ◽  
Frequent Pattern ◽  
Association Rules Mining ◽  
Classical Algorithm ◽  
Pattern Growth ◽  
Data Volume ◽  
Better Than

Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.

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