Updating mined class association rules for record insertion

Nguyen, Loan T. T.; Nguyen, Ngoc Thanh

doi:10.1007/s10489-014-0614-1

Cited by 18 publications

(9 citation statements)

References 24 publications

(53 reference statements)

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“…Specifically, in this study, we are interested in finding a set of association rules between two SQL query representations and use them to build an efficient classifier. Recent studies have shown that using classifiers based on class association rules resulted in higher accuracy than those obtained by using other classification algorithms such as C4.5 and ILA [29], which strengthens our motivation to evaluate CAR in our improved solution.…”

Section: Evaluating Classification Based On Association Rulessupporting

confidence: 68%

A Recommendation System for Execution Plans Using Machine Learning

Zahir

Qadi

2016

MCA

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Generating execution plans is a costly operation for the DataBase Management System (DBMS). An interesting alternative to this operation is to reuse the old execution plans, that were already generated by the optimizer for past queries, to execute new queries. In this paper, we present an approach for execution plan recommendation in two phases. We firstly propose a textual representation of our SQL queries and use it to build a Features Extractor module. Then, we present a straightforward solution to identify query similarity.This solution relies only on the comparison of the SQL statements. Next, we show how to build an improved solution enabled by machine learning techniques. The improved version takes into account the features of the queries' execution plans. By comparing three machine learning algorithms, we find that the improved solution using Classification Based on Associative Rules (CAR) identifies similarity in 91% of the cases.

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Section: Evaluating Classification Based On Association Rulessupporting

confidence: 68%

A Recommendation System for Execution Plans Using Machine Learning

Zahir

Qadi

2016

MCA

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“…The CMAR algorithm 27 is gradually developed on the basis of the classification-based association (CBA) method. 28 The CMAR algorithm and CBA algorithm are based on the Association Rules algorithm. CMAR is the transformation method of frequent pattern (FP)-growth that evolved from CBA, which can mine association rules sets satisfying the minimum support and minimum confidence, and several strong association rules are used to determine the class label of the new sample.…”

Section: Methods Of High-voltage Switchgearsfault-influencing Factors mentioning

confidence: 99%

A novel approach of fault-influencing factors analysis for high-voltage switchgears quality supervision based on industrial big data

Kong

Chang

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Fault-influencing factors analysis is an important part of the quality supervision process. There are double functions for high-voltage switchgears that switch off and protect electric circuits in power transmission lines. Such devices have serious impact on power grid–operating efficiency, factory operation, and resident life, which will cause economic losses. As it was difficult for traditional methods to analyze fault-influencing factors accurately and comprehensively, a novel method based on industrial big data was proposed to analyze high-voltage switchgears fault-influencing factors in the process of quality supervision in this article, which integrated the qualitative and quantitative analyses method. In this model, the Classification Based on Multiple Class-Association Rules based on Gaussian Mixture Model as the qualitative analysis method was adapted to analyze the whole life cycle of fault-influencing factors of high-voltage switchgears comprehensively, and supplied fault-influencing factors with discrete interval value ranges. The logistic regression method based on qualitative analysis was constructed to calculate fault occurrence probability quantitatively, including the single-fault occurrence probability and the multiple-faults joint occurrence probability. In addition, the single-fault occurrence probability was used to modify the discrete interval value ranges calculated by the qualitative analysis method, which could make the ranges more accurately. Consequently, the proposed method could provide important reference for high-voltage switchgears operation maintenance, and it would be possible to design accurate maintenance plans before equipment failure. The final instance demonstrates the effectiveness of the proposed methodology.

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“…Nguyen et al [19] modified equivalence class rules tree (MECR-tree) is formed from the real dataset. While records are put in, nodes on the tree are kept up to date by altering their information comprising Obidset, count, and pos.…”

Section: Literature Reviewmentioning

confidence: 99%

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

Ramesh¹,

Saravanan²

2018

PEN

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Fuzzy Class Association Rules (FCARs) play an important role in decision support systems and have thus been extensively studied. Mining the important rules in FCARs becomes very difficult task, so Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree) algorithm is proposed in this work. However, a major weakness of FCARs Miner is that when the number of constrained rules in a given class dominates the total constrained rules; its performance becomes slower than the normal method. To solve this problem this paper proposes a Proportion of Constraint Class Estimation (PPCE) algorithm for mining Enhanced Proportion Equivalence Fuzzy Constraint Class Association Rules (EPEFCARs) in order to save memory usage, run time and accuracy. Then, Proportion Frequency Occurrence count with Bat Algorithm (PFOCBA) is proposed for pruning rules which much satisfying the class constraints. Finally, an efficient algorithm is proposed for mining PEFCARs rules. Experimental results show that the proposed EPEFCR-tree algorithm is more efficient than Enhanced Equivalence Fuzzy Class Rule tree (EEFCRtree), Novel Equivalence Fuzzy Class Rule tree (NECR-tree) Miner results are measured in terms of run time, accuracy and memory usage. Experiments show that the proposed method is faster than existing methods.

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Updating mined class association rules for record insertion

Cited by 18 publications

References 24 publications

A Recommendation System for Execution Plans Using Machine Learning

A Recommendation System for Execution Plans Using Machine Learning

A novel approach of fault-influencing factors analysis for high-voltage switchgears quality supervision based on industrial big data

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

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