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.

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

Application of Association Rule Mining Technology in Collection and Management of Wireless Sensor Network Node

2014 ◽  
Vol 685 ◽  
pp. 575-578
Author(s):  
Guang Jiang Wang ◽  
Shi Guo Jin
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Wireless Sensor ◽  
Network Node ◽  
Rule Mining ◽  
Collection And Management

Association rule mining is an important data mining method; it is the key link of finding frequent itemsets. The process of association rules mining is roughly into two steps: the first step is to find out from all the concentration of all the frequent itemsets; the second step is to obtain the association rules from frequent itemsets. This paper analyzes the collected information of nodes in wireless sensor network and management. The paper presents application of association rule mining technology in the collection and management of wireless sensor network node.

Preserving Privacy in Mining Quantitative Associations Rules

2009 ◽  
Vol 3 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Madhu V. Ahluwalia ◽  
Aryya Gangopadhyay ◽  
Zhiyuan Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
State Of The Art ◽  
Academic Community ◽  
Discrete Wavelet ◽  
Rule Mining ◽  
Important Data

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

scholarly journals Association Rule Mining on Spambase Dataset using Tanagra

2020 ◽  
Vol 9 (6) ◽  
pp. 890-894
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Huge Amount ◽  
Memory Space ◽  
Better Than

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.

Flexible Mining of Association Rules

2011 ◽  
pp. 890-894
Author(s):  
Hong Shen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Strong Association ◽  
Frequent Itemsets ◽  
Efficient Algorithms ◽  
Multiple Level ◽  
Rule Mining ◽  
Unique Identifier

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)? T(milk). Let I ={x1,x2,…,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each record, T, has a unique identifier and contains a subset of items in I. We define itemset to be a set of items drawn from I and denote an itemset containing k items to be k-itemset. The support of itemset X, denoted by Ã(X/D), is the ratio of the number of records (in D) containing X to the total number of records in D. An association rule is an implication rule ?Y, where X; ? I and X ?Y=0. The confidence of ? Y is the ratio of s(?Y/D) to s(X/D), indicating that the percentage of those containing X also contain Y. Based on the user-specified minimum support (minsup) and confidence (minconf), the following statements are true: An itemset X is frequent if s(X/D)> minsup, and an association rule ? XY is strong i ?XY is frequent and ( / ) ( / ) X Y D X Y ? ¸ minconf. The problem of mining association rules is to find all strong association rules, which can be divided into two subproblems: 1. Find all the frequent itemsets. 2. Generate all strong rules from all frequent itemsets. Because the second subproblem is relatively straightforward ? we can solve it by extracting every subset from an itemset and examining the ratio of its support; most of the previous studies (Agrawal, Imielinski, & Swami, 1993; Agrawal, Mannila, Srikant, Toivonen, & Verkamo, 1996; Park, Chen, & Yu, 1995; Savasere, Omiecinski, & Navathe, 1995) emphasized on developing efficient algorithms for the first subproblem. This article introduces two important techniques for association rule mining: (a) finding N most frequent itemsets and (b) mining multiple-level association rules.

scholarly journals Analysis of the progressive sampling-based approach using real life datasets

2011 ◽  
Vol 1 (2) ◽  
Author(s):  
Venkatapathy Umarani ◽  
Muthusamy Punithavalli
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Life ◽  
Computation Time ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Databases ◽  
Very Large Databases ◽  
Progressive Sampling

AbstractThe discovery of association rules is an important and challenging data mining task. Most of the existing algorithms for finding association rules require multiple passes over the entire database, and I/O overhead incurred is extremely high for very large databases. An obvious approach to reduce the complexity of association rule mining is sampling. In recent times, several sampling-based approaches have been developed for speeding up the process of association rule mining. A proficient progressive sampling-based approach is presented for mining association rules from large databases. At first, frequent itemsets are mined from an initial sample and subsequently, the negative border is computed from the mined frequent itemsets. Based on the support computed for the midpoint itemset in the sorted negative border, the sample size is either increased or association rules are mined from it. In this paper, we have presented an extensive analysis of the progressive sampling-based approach with different real life datasets and, in addition, the performance of the approach is evaluated with the well-known association rule mining algorithm, Apriori. The experimental results show that accuracy and computation time of the progressive sampling-based approach is effectively improved in mining of association rules from the real life datasets.

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%. 

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

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.

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