scholarly journals Sistem Rekomendasi Penjualan Menu Makanan di UMKM Kuliner Menggunakan Association Rule

2021 ◽  
Vol 19 (2) ◽  
pp. 87-90
Author(s):  
Ade Kania Ningsih ◽  
Wina Witanti
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Apriori Algorithm ◽  
Process Data ◽  
Rule Mining ◽  
Confidence Threshold ◽  
Support Threshold ◽  
On Line

Micro, Small and Medium Enterprises (MSMEs) are one of the driving motors of the economy in the country, even MSMEs are the backbone of the Economy in Indonesia. MSMEs in Indonesia account for about 60% of GDP (Gross Domestic Product) and also provide employment opportunities to the community. However, with the emergence of THE COVID-19 outbreak of MSMEs in West Java there has been a decrease of up to 80%. This is a problem that exists, MSMEs customers are segmented based on the region due to large-scale social restrictions. This research conducted a review of product sales recommendation system in on-line shop using association rule mining in the culinary industry sector. The research begins with data selection, pre-process data, and data transformation, then the data that has been cleaned will be tested with A priori algorithm. The rules will evaluate using support, confidence, and an upgrade value to determine whether it's the best rule or not. The results of this study are software that will calculate the formation of association rules between culinary products. After an experiment with data amounting to 100 data, an association rule was obtained in the form of a certain pattern of customer behavior, by using Association Rules Technique and Apriori Algorithm, 12 rules are generated with a support threshold of 5% and a confidence threshold of 80%.  , Usaha Kecil dan Menengah (UMKM) merupakan salah satu motor penggerak perekonomian dalam negeri, bahkan UMKM merupakan tulang punggung Perekonomian di Indonesia. UMKM di Indonesia menyumbang sekitar 60% dari PDB (Produk Domestik Bruto) dan juga memberikan kesempatan kerja kepada masyarakat. Namun dengan munculnya Wabah COVID-19 pada UMKM di Jawa Barat terjadi penurunan hingga 80%. Hal ini menjadi permasalahan yang ada, nasabah UMKM tersegmentasi berdasarkan wilayah karena adanya pembatasan sosial berskala besar. Penelitian ini melakukan review terhadap sistem rekomendasi penjualan produk di toko on-line dengan menggunakan Association rule mining pada sektor industri kuliner. Penelitian diawali dengan pemilihan data, data praproses, dan transformasi data, kemudian data yang telah dibersihkan akan diuji dengan algoritma apriori. Aturan akan mengevaluasi menggunakan dukungan, keyakinan, dan nilai peningkatan untuk menentukan apakah itu aturan terbaik atau bukan. Hasil dari penelitian ini berupa software yang akan menghitung pembentukan aturan asosiasi antar produk kuliner. Setelah dilakukan percobaan dengan data sebanyak 100 data, diperoleh aturan asosiasi berupa pola perilaku konsumen tertentu, dengan menggunakan Association Rules Technique dan Apriori Algorithm dihasilkan 12 aturan dengan support threshold 5% dan confidence threshold. dari 80%. 

A New Algorithm of Association Rules Mining Based on Relation Matrix

2011 ◽  
Vol 179-180 ◽  
pp. 55-59
Author(s):  
Ping Shui Wang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Automatic Extraction ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Research Areas ◽  
Mining Algorithms ◽  
Relation Matrix

Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.

scholarly journals EARMGA and Apriori Algorithm's Performance Evaluation for Association Rule Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6982-6987
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Large Databases ◽  
Algorithm Comparison ◽  
Average Confidence ◽  
Mining Algorithms

Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.

scholarly journals Mining predicate rules without minimum support threshold

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Hafiz I. Ahmad ◽  
◽  
Alex T. H. Sim ◽  
Roliana Ibrahim ◽  
Mohammad Abrar ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Predicate Logic ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Minimum Support ◽  
Support Threshold ◽  
Definition Of

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

Association Rule Mining and Evaluation Based on Information Security Vulnerabilities Main Body

2014 ◽  
Vol 687-691 ◽  
pp. 1282-1285 ◽  
Author(s):  
Ying Sui
Keyword(s):  
Information Security ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Main Body ◽  
Apriori Algorithm ◽  
User Interest ◽  
Rule Mining ◽  
Security Vulnerabilities ◽  
Evaluation Stage

Information security is a matter of concern in any sector and industry, and the vulnerability is the important factor which caused this issue. Therefore it is necessary to analyze and predict the occurrence of vulnerability. This paper used the datas of CNNVD vulnerability database and Apriori algorithm to analyze and predict the occurrence of software vulnerability. In the data preprocessing stage by changing the level of vulnerability rule we can dig out more concept association. In the evaluation stage of association rules by designing filters we can find the rules in line with the degree of user interest. Finally, this papper could demonstrate the effectiveness of of this method by experiments.

Cross Sellingusing Association Rule Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1337-1341
Author(s):  
Ran Bo Yao ◽  
An Ping Song ◽  
Xue Hai Ding ◽  
Ming Bo Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
New Method ◽  
Experimental Result ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Direct Benefits ◽  
Frequent Item Sets

In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

An Efficient Communication Network SDH Alarm Association Rule Mining Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 1870-1873
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Event Window ◽  
Mining Algorithm ◽  
Efficient Communication ◽  
Algorithm Framework

WINEPI algorithm is kind of data mining technology that is widely used in alarm association rules mining. Based on the classic WINEPI algorithm, we apply event window instead of time window to improve the exploration result, meanwhile we use FP-Growth algorithm framework instead of Apriori algorithm framework , thus improving efficiency. Based on the alarm time attribute we find interesting alarm association rules further. Experiments show that compared with the classic WINEPI algorithm our improved approach have advantages in reducing the mining error rate and gaining more interesting alarm association rules.

Research and Application of Improved Apriori Algorithm Based on Matrix

2014 ◽  
Vol 668-669 ◽  
pp. 1102-1105
Author(s):  
Yuan Liu ◽  
Yuan Sheng Lou
Keyword(s):  
Decision Support ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Selection System ◽  
Course Selection ◽  
Rule Mining ◽  
Provide Decision Support

This article put forward a NCM_Apriori algorithm, which through compressing matrix and reducing the scan times to reduce the database I/O overhead, effectively improve the efficiency of association rule mining. At the same time in the process of generating association rules, computation is greatly reduced by using the nature of probability. And applies the algorithm to the mining of students' course selection system, which can provide decision support for colleges and universities.

Distributed elephant herding optimization for grid-based privacy association rule mining

2020 ◽  
Vol 54 (3) ◽  
pp. 365-382
Author(s):  
Praveen Kumar Gopagoni ◽  
Mohan Rao S K
Keyword(s):  
Association Rules ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemset ◽  
Association Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Content Type ◽  
Grid Based

PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

scholarly journals Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zhicong Kou ◽  
Lifeng Xi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Binary Particle Swarm Optimization ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Product Features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

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