Pub Date : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.26
F. Zhou, Xu Wang, Shan Chen, Yandong He, Lina Zhou
With the implementation of continues quality improvement procedure (QIP) in Chinese self-brand automotive firms, the quality-related cost needs identified correspondingly. Quality cost math models could reveal the mathematical relationship between the quality level and quality cost which provides the possibility for research on the trade-off of these two conflicting objectives, as well as contribute to quality related cost reduction. The quality index during QIP is established via Pearson coefficient within warranty period, related quality cost is categorized to conformance and non-conformance cost on the basis of the PAF ingredient as well. Four traditional quality cost math models have been analyzed in this paper, and the regression analysis based on curve fitting process has been implemented for a self-brand automotive firm during its QIP. The results verify that the four quality cost models (QCM) show their excellent simulating performance, which can uncover the optimal quality level and target R/1000@3MIS guiding correct operations during its QIP. In addition, the most appropriate quality performance level index is aggregated and calculated by employing a subjective AHP method, which specifies the quality improvement target ad potential cost reduction value.
{"title":"Research on Quality Cost Model (QCM) based on Quality Improvement Procedure (QIP) in an Auto-factory","authors":"F. Zhou, Xu Wang, Shan Chen, Yandong He, Lina Zhou","doi":"10.14257/ijdta.2017.10.1.26","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.26","url":null,"abstract":"With the implementation of continues quality improvement procedure (QIP) in Chinese self-brand automotive firms, the quality-related cost needs identified correspondingly. Quality cost math models could reveal the mathematical relationship between the quality level and quality cost which provides the possibility for research on the trade-off of these two conflicting objectives, as well as contribute to quality related cost reduction. The quality index during QIP is established via Pearson coefficient within warranty period, related quality cost is categorized to conformance and non-conformance cost on the basis of the PAF ingredient as well. Four traditional quality cost math models have been analyzed in this paper, and the regression analysis based on curve fitting process has been implemented for a self-brand automotive firm during its QIP. The results verify that the four quality cost models (QCM) show their excellent simulating performance, which can uncover the optimal quality level and target R/1000@3MIS guiding correct operations during its QIP. In addition, the most appropriate quality performance level index is aggregated and calculated by employing a subjective AHP method, which specifies the quality improvement target ad potential cost reduction value.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"249 1","pages":"285-298"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75052118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.01
Jiling Tang, P. Feng, Zhanlei Li
This paper analyzes the application of Chinese speech recognition technology in the non-barrier education system, and studies the construction of bi-modal database for barrier-free teaching system. Based on the case study of the curriculum named “Foundation of Photoshop”, the paper creates corpus to make acquisition of experimental data and annotation of corpora.Meanwhile we analyze and design the organization of data and build essential dictionary and grammar network in recognition system.
{"title":"Construction of Bi-modal Database for Barrier-free Teaching System","authors":"Jiling Tang, P. Feng, Zhanlei Li","doi":"10.14257/IJDTA.2017.10.1.01","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.01","url":null,"abstract":"This paper analyzes the application of Chinese speech recognition technology in the non-barrier education system, and studies the construction of bi-modal database for barrier-free teaching system. Based on the case study of the curriculum named “Foundation of Photoshop”, the paper creates corpus to make acquisition of experimental data and annotation of corpora.Meanwhile we analyze and design the organization of data and build essential dictionary and grammar network in recognition system.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"1 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84771933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.24
Fan Zhang, Yan Zhang, Haizhao Yuan, Chuanyu Sun, Yihang Li
The current agricultural machinery platforms just provide operational information of farmland and machinery, but not effective decision-making service. The problems of low utilization rate of agricultural machinery and low operation profits emerge as a major issue in the cross-regional operation of combine harvesters. The intelligent big data platform of agricultural machinery, which is firstly introduced, is not only to build an information exchanging platform for farmers and machine hand, but more important to provide the decision-making service. And then the deployment problem of combine harvesters is analyzed and the deployment model is established in the paper. Optimization deployment algorithm with global searching strategies, which is proposed in this paper, makes comparison with deployment algorithm with heuristic searching strategies that has be proposed in the author's previous article at aspects of deployment profit, cost and distances. It is concluded that the two algorithms have different applicable conditions. The better solution with high efficiency and performance can be obtained by the algorithm proposed in this paper.
{"title":"Research on Deployment Strategies of Combine Harvesters Based on Intelligent Big Data Platform","authors":"Fan Zhang, Yan Zhang, Haizhao Yuan, Chuanyu Sun, Yihang Li","doi":"10.14257/ijdta.2017.10.1.24","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.24","url":null,"abstract":"The current agricultural machinery platforms just provide operational information of farmland and machinery, but not effective decision-making service. The problems of low utilization rate of agricultural machinery and low operation profits emerge as a major issue in the cross-regional operation of combine harvesters. The intelligent big data platform of agricultural machinery, which is firstly introduced, is not only to build an information exchanging platform for farmers and machine hand, but more important to provide the decision-making service. And then the deployment problem of combine harvesters is analyzed and the deployment model is established in the paper. Optimization deployment algorithm with global searching strategies, which is proposed in this paper, makes comparison with deployment algorithm with heuristic searching strategies that has be proposed in the author's previous article at aspects of deployment profit, cost and distances. It is concluded that the two algorithms have different applicable conditions. The better solution with high efficiency and performance can be obtained by the algorithm proposed in this paper.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"47 1","pages":"259-270"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83248313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.09
Xiong Wenjun, Xu Zhengquan, Hao Wang
Differential privacy has played a significant role in privacy preserving, and it has performed well in independent series. However, in real-world applications, most data are released in the form of correlated time series. Although a few differential privacy methods have focused on correlated time series, they are not designed by protecting against a specific attack model. Due to this drawback, the effectiveness of these methods cannot be verified and the privacy level of them cannot be measured. To address the problem, this paper presents an attack model based on the principle of filtering in signal processing theory. Since the distribution of the noise designed by current methods is independent and different from that of the original correlated series, a filter is designed as a unified attack model to sanitize the independent noise from the perturbed time series. Furthermore, the designed attack model can realize the function of measuring the effective privacy level of these methods and comparing the performance of them. Experimental results show that the attack model leads to degradation in privacy levels and can work as a unified measurement.
{"title":"An Attack Model on Differential Privacy Preserving Methods for Correlated Time Series","authors":"Xiong Wenjun, Xu Zhengquan, Hao Wang","doi":"10.14257/IJDTA.2017.10.1.09","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.09","url":null,"abstract":"Differential privacy has played a significant role in privacy preserving, and it has performed well in independent series. However, in real-world applications, most data are released in the form of correlated time series. Although a few differential privacy methods have focused on correlated time series, they are not designed by protecting against a specific attack model. Due to this drawback, the effectiveness of these methods cannot be verified and the privacy level of them cannot be measured. To address the problem, this paper presents an attack model based on the principle of filtering in signal processing theory. Since the distribution of the noise designed by current methods is independent and different from that of the original correlated series, a filter is designed as a unified attack model to sanitize the independent noise from the perturbed time series. Furthermore, the designed attack model can realize the function of measuring the effective privacy level of these methods and comparing the performance of them. Experimental results show that the attack model leads to degradation in privacy levels and can work as a unified measurement.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"100 1","pages":"89-104"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88962807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.08
Liyun Zhong
Kernel-based learning methods (kernel methods for short) in general and support vector machine (SVM) in particular have been successfully applied to the task of text classification. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. A significant challenge in text classification is to reduce the need for labeled training data while maintaining an acceptable performance. This paper presents a semi-supervised technique using the exponential kernel for text classification. Specifically, the semantic similarities between terms are first determined with both labeled and unlabeled training data by means of a diffusion process on a graph defined by lexicon and co-occurrence information, and the exponential kernel is then constructed based on the learned semantic similarity. Finally, the SVM classifier trains a model for each class during the training phase and this model is then applied to all test examples in the test phase. The main feature of this approach is that it takes advantage of the exponential kernel to reveal the semantic similarities between terms in an unsupervised manner, which provides a kernel framework for semi-supervised learning. The proposed approach is demonstrated on several benchmark data sets for text classification and the experimental results show that it can significantly improve the classification performance.
{"title":"Semi-supervised Text Classification Using SVM with Exponential Kernel","authors":"Liyun Zhong","doi":"10.14257/IJDTA.2017.10.1.08","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.08","url":null,"abstract":"Kernel-based learning methods (kernel methods for short) in general and support vector machine (SVM) in particular have been successfully applied to the task of text classification. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. A significant challenge in text classification is to reduce the need for labeled training data while maintaining an acceptable performance. This paper presents a semi-supervised technique using the exponential kernel for text classification. Specifically, the semantic similarities between terms are first determined with both labeled and unlabeled training data by means of a diffusion process on a graph defined by lexicon and co-occurrence information, and the exponential kernel is then constructed based on the learned semantic similarity. Finally, the SVM classifier trains a model for each class during the training phase and this model is then applied to all test examples in the test phase. The main feature of this approach is that it takes advantage of the exponential kernel to reveal the semantic similarities between terms in an unsupervised manner, which provides a kernel framework for semi-supervised learning. The proposed approach is demonstrated on several benchmark data sets for text classification and the experimental results show that it can significantly improve the classification performance.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"49 1","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80479512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.25
Hong-Tao Liu
Efficient processing platform can effectively analyze massive data, strong support for data mining algorithms and data visualization. In this paper, the authors use the new path of integration earnings management way and earnings management direction to study the relationship between China’s listed companies’ ownership structure and earnings management. The results found that: private holding company's earnings quality the pressure is far greater than the state-owned holding company; negative earnings management private holding company level was significantly lower than the state-owned holding company; U-shaped relationship between ownership concentration and earnings management, moderate concentration of ownership in favor of reducing the level of earnings management. Accordingly, aspects of equity nature, are intended to promote the development direction of mixed ownership in line with our national interests; the controlling stake in moderate levels of concentration of ownership structure another sign of deepening reform success.
{"title":"Network Data Mining Application in Earnings Management of Private Holding Enterprise: An Empirical Analysis Based on Multiple Regression Model","authors":"Hong-Tao Liu","doi":"10.14257/IJDTA.2017.10.1.25","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.25","url":null,"abstract":"Efficient processing platform can effectively analyze massive data, strong support for data mining algorithms and data visualization. In this paper, the authors use the new path of integration earnings management way and earnings management direction to study the relationship between China’s listed companies’ ownership structure and earnings management. The results found that: private holding company's earnings quality the pressure is far greater than the state-owned holding company; negative earnings management private holding company level was significantly lower than the state-owned holding company; U-shaped relationship between ownership concentration and earnings management, moderate concentration of ownership in favor of reducing the level of earnings management. Accordingly, aspects of equity nature, are intended to promote the development direction of mixed ownership in line with our national interests; the controlling stake in moderate levels of concentration of ownership structure another sign of deepening reform success.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"48 1","pages":"271-284"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88875305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.19
Baomin Xu, Jie Huang, Yang Yang
Suffix arrays is versatile data structures playing a key role in numerous string processing applications such as the data structure can be used to represent the given DNA strings. However, the most serious drawback of suffix arrays is their size, namely space usage. In this paper, we propose a new suffix array compression technique, i.e., numerical method for suffix array index compression, for the problem. With the method, we will translate DNA bases characters ATGC to the corresponding integer number 1234. The experimental results show that the numerical method for suffix array index compression not only can greatly compress the memory space of suffix array, but also can retain the quick search characteristics of suffix array.
{"title":"A Numerical Method for Suffix Array Index Compression","authors":"Baomin Xu, Jie Huang, Yang Yang","doi":"10.14257/IJDTA.2017.10.1.19","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.19","url":null,"abstract":"Suffix arrays is versatile data structures playing a key role in numerous string processing applications such as the data structure can be used to represent the given DNA strings. However, the most serious drawback of suffix arrays is their size, namely space usage. In this paper, we propose a new suffix array compression technique, i.e., numerical method for suffix array index compression, for the problem. With the method, we will translate DNA bases characters ATGC to the corresponding integer number 1234. The experimental results show that the numerical method for suffix array index compression not only can greatly compress the memory space of suffix array, but also can retain the quick search characteristics of suffix array.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"48 1","pages":"207-212"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91220345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.07
Geetika Munjal, M. Hanmandlu, Sangeet Srivastva, D. Gaur
Assessing and Mining phylogenetic trees is very useful in storing, querying the phylogenetic databases, and finding an accurate phylogenetic tree for a set of species is very difficult. Assessing a phylogenetic tree also resolves the problem of conflicting phylogenies. This paper discusses the methods for validating and mining phylogenetic trees. We propose a new way to compare two trees by accessing importance of node in tree. This new method is applied on phylogenetic trees and the results compared with symmetric distance, Maximum Agreement Subtree and Bootstrapped tree.
{"title":"Assessing and Mining of Phylogenetic Trees","authors":"Geetika Munjal, M. Hanmandlu, Sangeet Srivastva, D. Gaur","doi":"10.14257/IJDTA.2017.10.1.07","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.07","url":null,"abstract":"Assessing and Mining phylogenetic trees is very useful in storing, querying the phylogenetic databases, and finding an accurate phylogenetic tree for a set of species is very difficult. Assessing a phylogenetic tree also resolves the problem of conflicting phylogenies. This paper discusses the methods for validating and mining phylogenetic trees. We propose a new way to compare two trees by accessing importance of node in tree. This new method is applied on phylogenetic trees and the results compared with symmetric distance, Maximum Agreement Subtree and Bootstrapped tree.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"22 1","pages":"67-78"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81754467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.03
Jin Zhao, Runtao Lv, Yu Li
This paper mentions several interestingness measures as Lift, Conviction, Piatetsky-Shapiro, Cosine, Jaccard and so on, which have proposed for mining association rules and classification rules but they have not been applied to mine sequential rules in sequence databases except the traditional measures of rule such as the support and confidence. We also propose then an efficient algorithm to generate all relevant sequential rules with the above interestingness measures from the prefix-tree which stored the whole sequential pattern where each child node stores a sequential pattern and its corresponding support value. By traversing the prefix-tree, the algorithm can then easily identify the components of a rule, and can calculate the measured values of the rule. The experimental results show that sequential rule mining with interestingness measures using the proposed algorithm based on the prefix-tree was always much faster than that using the other existing algorithm as modified Full. Especially when mining in large sequence databases with the low minimum support values, the number of sequential patterns generated from sequence databases was large and the proposed algorithm outperformed much because the proposed algorithm only traverse the prefix-tree to immediately determine which sequences are the left- and right-hand sides of a rule as well as their support values to compute the interestingness measure values of the rule from the sequential pattern set. In addition, the experimental results also show that the time for mining sequential rules with the confidence measure was the smallest, because it did not need to revisit the prefix-tree to determine the support of Y (the antecedence of rules), while the other interestingness measures need to revisit the prefix-tree to determine the support values of the consequent of rules or both the antecedence and the consequent.
{"title":"An Improved Sequential Pattern Algorithm Based on Data Mining","authors":"Jin Zhao, Runtao Lv, Yu Li","doi":"10.14257/IJDTA.2017.10.1.03","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.03","url":null,"abstract":"This paper mentions several interestingness measures as Lift, Conviction, Piatetsky-Shapiro, Cosine, Jaccard and so on, which have proposed for mining association rules and classification rules but they have not been applied to mine sequential rules in sequence databases except the traditional measures of rule such as the support and confidence. We also propose then an efficient algorithm to generate all relevant sequential rules with the above interestingness measures from the prefix-tree which stored the whole sequential pattern where each child node stores a sequential pattern and its corresponding support value. By traversing the prefix-tree, the algorithm can then easily identify the components of a rule, and can calculate the measured values of the rule. The experimental results show that sequential rule mining with interestingness measures using the proposed algorithm based on the prefix-tree was always much faster than that using the other existing algorithm as modified Full. Especially when mining in large sequence databases with the low minimum support values, the number of sequential patterns generated from sequence databases was large and the proposed algorithm outperformed much because the proposed algorithm only traverse the prefix-tree to immediately determine which sequences are the left- and right-hand sides of a rule as well as their support values to compute the interestingness measure values of the rule from the sequential pattern set. In addition, the experimental results also show that the time for mining sequential rules with the confidence measure was the smallest, because it did not need to revisit the prefix-tree to determine the support of Y (the antecedence of rules), while the other interestingness measures need to revisit the prefix-tree to determine the support values of the consequent of rules or both the antecedence and the consequent.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"18 1","pages":"23-36"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87893797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.06
Zhang Xuewu
Such problems as premature convergence and local optimal solution universally exist in the application of traditional genetic algorithm to the association rules mining, so a lot of time is needed for extracting the useful strong association rules. In order to conquer these disadvantages, the adaptive variation rate is introduced in this paper and the method for the operator selection during the genetic process is improved in order to specifically improve the traditional genetic algorithm, and the improved association rules mining method is used to analyze the power transformation equipment defect data. The example comparison shows that the improved genetic algorithm can significantly reduce the rule discovery calculation complexity and improve the association rules mining efficiency.
{"title":"Temporal and Spatial Association Rules Strong Mining Algorithm Based on Hierarchical Reasoning Parameters","authors":"Zhang Xuewu","doi":"10.14257/ijdta.2017.10.1.06","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.06","url":null,"abstract":"Such problems as premature convergence and local optimal solution universally exist in the application of traditional genetic algorithm to the association rules mining, so a lot of time is needed for extracting the useful strong association rules. In order to conquer these disadvantages, the adaptive variation rate is introduced in this paper and the method for the operator selection during the genetic process is improved in order to specifically improve the traditional genetic algorithm, and the improved association rules mining method is used to analyze the power transformation equipment defect data. The example comparison shows that the improved genetic algorithm can significantly reduce the rule discovery calculation complexity and improve the association rules mining efficiency.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"359 1","pages":"57-66"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76410317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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