Association Rules Analysis on FP-Growth Method in Predicting Sales

Penyandang Masalah Kesejahteraan Sosial (PMKS) menjadi satu dari sekian masalah yang terdapat di daerah perkotaan, sebab dapat mengganggu pembangunan kota, ketertiban umum, keamanan dan stabilitas. Sejauh ini langkah yang dilakukan sementara masih terfokus dengan cara penanganan PMKS, masih belum mengarah untuk mencegah. Menentukan pola golongan PMKS merupakan salah satu cara yang dapat dilakukan. Algoritma Apriori memiliki fungsi untuk membantu menemukan pola yang terdapat pada data (frequent pattern mining) untuk menentukan frequent itemset yang menggunakan metode Association Rule dalam data mining. Dalam penghitungan secara manual yang dilakukan maka didapat pola kombinasi antara lain 3 rules yang memiliki nilai minimum support 15% dengan confidence tertinggi 100% menggunakan Algoritma Apriori. Dalam menguji Algoritma Apriori digunakan aplikasi RapidMiner. RapidMiner merupakan satu dari beberapa software pengolah data mining, misalnya menganalisis teks, mengekstrak pola data set kemudian dikombinasikan menggunakan metode statistik, database, dan kecerdasan buatan agar didapat informasi yang tinggi berasal dari olahan data. Hasil yang didapat dari pengujian perbandingan pola antar golongan PMKS. Dari pengujian menggunakan aplikasi RapidMiner dan penghitungan secara manual Algoritma Apriori, maka disimpulkan dengan kriteria pengujian, bahwa pola (rules) golongan dengan nilai confidence (c) penghitungan manual Algoritma Apriori dapat dibilang tidak mendekati hasil pengujian aplikasi RapidMiner, maka dapat dikatakan tingkat keakuratan pengujian rencah, hanya 37,5%.

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Penerapan Data Mining Menggunakan Algoritma Apriori untuk Menentukan Pola Penyebab Gelandangan dan Pengemis

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020721376 ◽

2020 ◽

Vol 7 (2) ◽

pp. 229

Author(s):

Wirta Agustin ◽

Yulya Muharmi

Keyword(s):

Data Mining ◽

Association Rule ◽

Urban Areas ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Itemset ◽

Frequent Pattern ◽

Data Sets ◽

Apriori Algorithm ◽

Data Set

Gelandangan dan pengemis salah satu masalah yang ada di daerah perkotaan, karena dapat mengganggu ketertiban umum, keamanan, stabilitas dan pembangunan kota. Upaya yang dilakukan saat ini masih fokus pada cara penanganan gelandangan dan pengemis, belum untuk pencegahan. Salah satu cara yang bisa dilakukan adalah dengan menentukan pola usia gelandangan dan pengemis. Algoritma Apriori sebuah metode Association Rule dalam data mining untuk menentukan frequent itemset yang berfungsi membantu menemukan pola dalam sebuah data (frequent pattern mining). Perhitungan manual menggunakan algoritma apriori, menghasilkan pola kombinasi sebanyak 3 rules dengan nilai minimum support sebesar 30% dan nilai confidence tertinggi sebesar 100%. Pengujian penerapan Algoritma Apriori menggunakan aplikasi RapidMiner. RapidMiner salah satu software pengolahan data mining, diantaranya analisis teks, mengekstrak pola-pola dari data set dan mengkombinasikannya dengan metode statistika, kecerdasan buatan, dan database untuk mendapatkan informasi bermutu tinggi dari data yang diolah. Hasil pengujian menunjukkan perbandingan pola usia gelandangan dan pengemis yang berpotensi menjadi gelandangan dan pengemis. Berdasarkan hasil pengujian aplikasi RapidMiner dan hasil perhitungan manual Algoritma Apriori, dapat disimpulkan sesuai kriteria pengujian, bahiwa pola (rules) usia dan nilai confidence (c) hasil perhitungan manual Algoritma Apriori tidak mendekati nilai hasil pengujian menggunakan aplikasi RapidMiner, maka tingkat keakuratan pengujian rendah, yaitu 37.5 %. Abstract Homeless and beggars are one of the problems in urban areas as they possibly disrupt public order, security, stability and urban development. The efforts conducted are still focusing on managing the existing homeless and beggars instead of preventing the potential ones. One of the methods used for solving this problem is Algoritma Apriori which determines the age pattern of homeless and beggars. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtains combination pattern of 3 rules with a minimum support value of 30% and the highest confidence value of 100%. These patterns were refences for the incharged department in precaution action of homeless and beggars arising numbers. Apriori Algorithm testing uses the RapidMiner application which is one of data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. Based on the results of the said testing, it can be concluded that the level of accuracy test is low, i.e. 37.5%.

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A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.145.292 ◽

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.

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Finding Persistent Strong Rules

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch005 ◽

2010 ◽

pp. 85-107

Author(s):

Anthony Scime ◽

Karthik Rajasethupathy ◽

Kulathur S. Rajasethupathy ◽

Gregg R. Murray

Keyword(s):

Data Mining ◽

Association Rules ◽

Strong Association ◽

National Election ◽

Data Sets ◽

Rule Discovery ◽

Discovery Process ◽

Data Set ◽

Rule Sets ◽

Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

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Visual Data Mining for Discovering Association Rules

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch125 ◽

2008 ◽

pp. 2105-2120

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Visual Data Mining for Discovering Association Rules

Business Applications and Computational Intelligence ◽

10.4018/978-1-59140-702-7.ch011 ◽

2011 ◽

pp. 209-226

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.998-999.842 ◽

2014 ◽

Vol 998-999 ◽

pp. 842-845 ◽

Cited By ~ 1

Author(s):

Jia Mei Guo ◽

Yin Xiang Pei

Keyword(s):

Data Mining ◽

Association Rules ◽

Clustering Algorithm ◽

Original Data ◽

Data Set ◽

Association Rules Mining ◽

Fuzzy Association Rules ◽

Redundant Data ◽

Fuzzy Partitions ◽

Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

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FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

Computing Letters ◽

10.1163/1574040054861285 ◽

2005 ◽

Vol 1 (3) ◽

pp. 129-135

Author(s):

Jun Luo ◽

Sanguthevar Rajasekaran

Keyword(s):

Data Mining ◽

Association Rules ◽

Fast Algorithm ◽

Frequent Itemsets ◽

Experimental Results ◽

Important Data ◽

Computational Performance ◽

Large Databases ◽

Intersection Operation ◽

Better Than

Association rules mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attributes lists using hash Tables (FIT) is presented. FIT is designed for efficiently computing all the frequent itemsets in large databases. It deploys an idea similar to Eclat but has a much better computational performance than Eclat due to two reasons: 1) FIT makes fewer total number of comparisons for each intersection operation between two attributes lists, and 2) FIT significantly reduces the total number of intersection operations. Our experimental results demonstrate that the performance of FIT is much better than that of Eclat and Apriori algorithms.

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Association Rules Optimization Algorithm Based on Fuzzy Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3536 ◽

2014 ◽

Vol 602-605 ◽

pp. 3536-3539

Author(s):

Yu Fu ◽

Jun Rui Yang

Keyword(s):

Association Rules ◽

Fuzzy Clustering ◽

Pattern Mining ◽

Computing Time ◽

Frequent Pattern Mining ◽

Research Direction ◽

Frequent Itemset ◽

Frequent Pattern ◽

Good Prospect ◽

Original Dataset

Frequent pattern mining has been an important research direction in association rules. This paper use a methodology by preprocessing the original dataset using fuzzy clustering which can mapped quantitative datasets into linguistic datasets. Then we propose a algorithm based on fuzzy frequent pattern tree for extracting fuzzy frequent itemset from mapped linguistic datasets. Experimental results show that our algorithm is shorter than the F-Apriori on computing time to huge database. For large database, the algorithm presented in this paper is proved to have a good prospect.

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Marketing Strategy Using Frequent Pattern Growth

Journal Of Computer Networks, Architecture and High Performance Computing ◽

10.47709/cnahpc.v3i2.1039 ◽

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.

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