scholarly journals Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4228
Author(s):  
Yan Xu ◽  
Mingyu Wang ◽  
Wen Fan
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Weighted Average ◽  
Screening Strategy ◽  
Apriori Algorithm ◽  
Secondary System ◽  
Fireworks Algorithm ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Data Coverage

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results.

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%. 

Closed-Itemset Incremental-Mining Problem

2011 ◽  
pp. 150-153
Author(s):  
Luminita Dumitriu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Incremental Mining ◽  
Minimum Support ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

Research and Application of Apriori Algorithm for Mining Association Rules

2014 ◽  
Vol 1079-1080 ◽  
pp. 737-742
Author(s):  
Yi Yong Ye
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Web Mining ◽  
Recommendation System ◽  
Apriori Algorithm ◽  
Mining Algorithm ◽  
Common Technique ◽  
Mining Association Rules

For large amounts of data generated by the e-commerceplatform, combining with the actual needs of e-commerce recommendation system,make research on a common technique of association rules which orientede-commerce Web mining association analysis, introduces the association rules ofApriori mining algorithm, and the specific application of Apriori algorithm isanalyzed through a practical example, Finally, point out the shortcomings ofclassical Apriori algorithm, and gives directions for improvement.

scholarly journals Association analysis in food sampling inspection data

2022 ◽  
Vol 355 ◽  
pp. 02033
Author(s):  
Tongqiang Jiang ◽  
Xin Chen ◽  
Huan Jiang
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Work Efficiency ◽  
Food Category ◽  
Sampling Inspection ◽  
Inspection Data ◽  
The Cost

At present, China exists a problem that the cost of food sampling inspection is too high. This paper attempts to reduce the number of sampling inspection items in the same food category, reduce the cost of food sampling inspection, and improve the work efficiency through the association analysis of national sampling inspection data. And this paper applies Apriori algorithm to analyse the association rules, which is based on the unqualified pastry sampling inspection data in the 2019 national food sampling inspection database. Finally, we obtain 10 strong association rules through experiments. The results show that this association analysis can reduce the workload of food sampling inspection effectively.

The Research of Improving Clothing Crossing-Selling Based on Apriori Arithmetic

2014 ◽  
Vol 989-994 ◽  
pp. 1586-1589
Author(s):  
Chi Zhang ◽  
Yi Liu ◽  
Fang Shuai Sun ◽  
Li Yao
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Commercial Test ◽  
Sales Data ◽  
Clothing Store ◽  
Specific Association

First, we describe crossing-selling and association rules. And then with the study of a correlation clothing store sales data, it shows the Apriori algorithm applies in specific association analysis. We can propose a model which is suitable for crossing-selling. Through commercial test, the algorithm can significantly increase sales of the relevant product.

scholarly journals The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255684
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng ◽  
Yuping Han
Keyword(s):  
Water Supply ◽  
Association Analysis ◽  
Association Rules ◽  
Strong Association ◽  
Continuous Data ◽  
Apriori Algorithm ◽  
Analysis Method ◽  
Attribution Analysis ◽  
The One ◽  
Data Discretization

Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.

Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse

2008 ◽  
pp. 3222-3234
Author(s):  
Yun Sing Koh ◽  
Nathan Rountree ◽  
Richard O’Keefe
Keyword(s):  
Data Mining ◽  
Rare Disease ◽  
Association Rules ◽  
Frequent Itemsets ◽  
New Method ◽  
Apriori Algorithm ◽  
High Confidence ◽  
Minimum Support ◽  
Rare Association ◽  
Support Threshold

Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. To alleviate this problem, we propose a new method of discovering sporadic rules without having to produce all other rules above the minimum support threshold. The new method, called Apriori-Inverse, is a variation of the Apriori algorithm that uses the notion of maximum support instead of minimum support to generate candidate itemsets. Candidate itemsets of interest to us fall below a maximum support value but above a minimum absolute support value. Rules above maximum support are considered frequent rules, which are of no interest to us, whereas rules that occur by chance fall below the minimum absolute support value. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). This article is an expanded version of Koh and Rountree (2005).

Improved Apriori Algorithm in the Power System Fault

2014 ◽  
Vol 556-562 ◽  
pp. 1510-1514
Author(s):  
Li Qiang Lin ◽  
Hong Wen Yan
Keyword(s):  
Power System ◽  
Association Rules ◽  
Fault Analysis ◽  
The Other ◽  
Apriori Algorithm ◽  
Extreme Climate ◽  
Time Space ◽  
Low Efficiency ◽  
Candidate Item ◽  
Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

2021 ◽  
Vol 30 (04) ◽  
pp. 2150018
Author(s):  
Anindita Borah ◽  
Bhabesh Nath
Keyword(s):  
Air Pollution ◽  
Association Rules ◽  
Air Pollutants ◽  
Pattern Mining ◽  
Environmental Parameters ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rare Association ◽  
Support Threshold ◽  
Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

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