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Journal articles on the topic 'Mining software engineering data'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Halkidi, M., D. Spinellis, G. Tsatsaronis, and M. Vazirgiannis. "Data mining in software engineering." Intelligent Data Analysis 15, no. 3 (May 4, 2011): 413–41. http://dx.doi.org/10.3233/ida-2010-0475.

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2

Xie, Tao, Suresh Thummalapenta, David Lo, and Chao Liu. "Data Mining for Software Engineering." Computer 42, no. 7 (August 2009): 55–62. http://dx.doi.org/10.1109/mc.2009.256.

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3

Hall, Robert J. "Editorial: data mining in software engineering." Automated Software Engineering 17, no. 4 (July 13, 2010): 373–74. http://dx.doi.org/10.1007/s10515-010-0073-9.

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4

Marbán, Oscar, Javier Segovia, Ernestina Menasalvas, and Covadonga Fernández-Baizán. "Toward data mining engineering: A software engineering approach." Information Systems 34, no. 1 (March 2009): 87–107. http://dx.doi.org/10.1016/j.is.2008.04.003.

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5

Taylor, Quinn, Christophe Giraud Carrier, and Charles D. Knutson. "Applications of data mining in software engineering." International Journal of Data Analysis Techniques and Strategies 2, no. 3 (2010): 243. http://dx.doi.org/10.1504/ijdats.2010.034058.

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Periasamy, A. R. Pon, and A. Mishbahulhuda. "Applications of Data Mining Techniques in Software Engineering." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (March 30, 2017): 304–7. http://dx.doi.org/10.23956/ijarcsse/v7i3/0174.

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7

Canaparo, Marco, and Elisabetta Ronchieri. "Data Mining Techniques for Software Quality Prediction in Open Source Software." EPJ Web of Conferences 214 (2019): 05007. http://dx.doi.org/10.1051/epjconf/201921405007.

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Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models.

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KAJKO-MATTSSON, MIRA, and NED CHAPIN. "DATA MINING FOR VALIDATION IN SOFTWARE ENGINEERING: AN EXAMPLE." International Journal of Software Engineering and Knowledge Engineering 14, no. 04 (August 2004): 407–27. http://dx.doi.org/10.1142/s0218194004001725.

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Consider two independently done software engineering studies that used different approaches to cover some of the same subject area, such as software maintenance. Although done differently and for different purposes, to what extent can each study serve as a validation of the other? Within the scope of the subject area overlap, data mining can be applied to provide a quantitative assessment. This paper reports on the data mining that attempted to cross validate two independently done and published software engineering studies of software maintenance, one on a corrective maintenance maturity model, and the other on an objective classification of software maintenance activities. The data mining established that each of the two independently done studies effectively and very strongly validates the other.

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KHOSHGOFTAAR, TAGHI M., EDWARD B. ALLEN, WENDELL D. JONES, and JOHN P. HUDEPOHL. "DATA MINING FOR PREDICTORS OF SOFTWARE QUALITY." International Journal of Software Engineering and Knowledge Engineering 09, no. 05 (October 1999): 547–63. http://dx.doi.org/10.1142/s0218194099000309.

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"Knowledge discovery in data bases" (KDD) for software engineering is a process for finding useful information in the large volumes of data that are a byproduct of software development, such as data bases for configuration management and for problem reporting. This paper presents guidelines for extracting innovative process metrics from these commonly available data bases. This paper also adapts the Classification And Regression Trees algorithm, CART, to the KDD process for software engineering data. To our knowledge, this algorithm has not been used previously for empirical software quality modeling. In particular, we present an innovative way to control the balance between misclassification rates. A KDD case study of a very large legacy telecommunications software system found that variables derived from source code, configuration management transactions, and problem reporting transactions can be useful predictors of software quality. The KDD process discovered that for this software development environment, out of forty software attributes, only a few of the predictor variables were significant. This resulted in a model that predicts whether modules are likely to have faults discovered by customers. Software developers need such predictions early in development to target software enhancement techniques to the modules that need improvement the most.

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Minku, Leandro L., Emilia Mendes, and Burak Turhan. "Data mining for software engineering and humans in the loop." Progress in Artificial Intelligence 5, no. 4 (April 16, 2016): 307–14. http://dx.doi.org/10.1007/s13748-016-0092-2.

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NAYAK, RICHI, and TIAN QIU. "A DATA MINING APPLICATION: ANALYSIS OF PROBLEMS OCCURRING DURING A SOFTWARE PROJECT DEVELOPMENT PROCESS." International Journal of Software Engineering and Knowledge Engineering 15, no. 04 (August 2005): 647–63. http://dx.doi.org/10.1142/s0218194005002476.

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Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.

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Morejón, Reinier, Marx Viana, and Carlos Lucena. "An Approach to Generate Software Agents for Health Data Mining." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1579–89. http://dx.doi.org/10.1142/s0218194017400125.

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Data mining is a hot topic that attracts researchers of different areas, such as database, machine learning, and agent-oriented software engineering. As a consequence of the growth of data volume, there is an increasing need to obtain knowledge from these large datasets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing, and integration of data as well as parallel, distributed, or multisource mining. This paper proposes a framework based on multiagent systems to apply data mining techniques to health datasets. Last but not least, the usage scenarios that we use are datasets for hypothyroidism and diabetes and we run two different mining processes in parallel in each database.

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Mahmood, Nasir, Yaser Hafeez, Khalid Iqbal, Shariq Hussain, Muhammad Aqib, Muhammad Jamal, and Oh-Young Song. "Mining Software Repository for Cleaning Bugs Using Data Mining Technique." Computers, Materials & Continua 69, no. 1 (2021): 873–93. http://dx.doi.org/10.32604/cmc.2021.016614.

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Zhang, Qingyu, and Richard S. Segall. "Review of data, text and web mining software." Kybernetes 39, no. 4 (May 4, 2010): 625–55. http://dx.doi.org/10.1108/03684921011036835.

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15

Ren, Jun-Hua, and Feng Liu. "Predicting Software Defects Using Self-Organizing Data Mining." IEEE Access 7 (2019): 122796–810. http://dx.doi.org/10.1109/access.2019.2927489.

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Cui, Zhihao, and Chaobing Yan. "Deep Integration of Health Information Service System and Data Mining Analysis Technology." Applied Mathematics and Nonlinear Sciences 5, no. 2 (December 21, 2020): 443–52. http://dx.doi.org/10.2478/amns.2020.2.00063.

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AbstractThe scale and complexity of health information service system has increased dramatically, and its development activities and management are difficult to control. In the field of, Traditional methods and simple mathematical statistics methods are difficult to solve the problems caused by the explosive growth of data and information, which will adversely affect health information service system management finally. So, it is particularly important to find valuable information from the source code, design documents and collected software datasets and to guide the development and maintenance of software engineering. Therefore, some experts and scholars want to use mature data mining technologies to study the large amount of data generated in software engineering projects (commonly referred to as software knowledge base), and further explore the potential and valuable information inherently hidden behind the software data. This article initially gives a brief overview of the relevant knowledge of data mining technology and computer software technology, using decision tree graph mining algorithm to mine the function adjustment graph of the software system definition class, and then source code annotations are added to the relevant calling relationships. Data mining technology and computer software technology are deeply integrated, and the decision tree algorithm in data mining is used to mine the knowledge base of computer software. Potential defect changes are listed as key maintenance objects. The historical versions of source code change files with defects are found dynamically and corrected in time, to avoid the increase of maintenance cost in the future.

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Voinea, Lucian, and Alexandru Telea. "Visual data mining and analysis of software repositories." Computers & Graphics 31, no. 3 (June 2007): 410–28. http://dx.doi.org/10.1016/j.cag.2007.01.031.

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18

Luo, Rong Liang. "Application of Data Mining in Data Analysis of Tobacco Consumption." Advanced Materials Research 282-283 (July 2011): 770–73. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.770.

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Development of data mining technology provides convenience for analyzing tobacco consumers’ act. Through simple introduction on contents and categories of data mining technology, the survey on tobacco consumption act of Shaoxin Tobacco Company is analyzed with association rules and data mining software Weka, and factors which affect tobacco consumption are mined on with association with Apriori Algorithm, so as to provide valuable references for brand spreading channels, product design, improvement of taste and flavor, package, price and other aspects for the tobacco company.

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Et. al., J. Mary Catherine,. "A STUDY ON SOFTWARE DEFECT PREDICTION SYSTEM USING DATA MINING TECHNIQUES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 11, 2021): 950–55. http://dx.doi.org/10.17762/itii.v9i2.435.

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Defects in software modules are a source of significant concern. Software reliability and software quality assurance ensure the high quality of applications. A software defect triggers software malfunction in an executable product. A number of methods for forecasting machine faults have been suggested, but none have proven to be sufficiently accurate. In the design of software error prediction models, the aim is to use metrics that can be obtained comparatively early in the life cycle of software production to provide fair initial quality estimates of an evolving software framework.Here are various data mining classification and forecasting techniques. Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) have been analyzed and compared for software defect prediction model development. For this paper, the DATATRIEVETM project developed by Digital Engineering, Italy was used to validate the algorithm. The findings revealed that the model was an exceptional statistical model using the NN classification methodology. The main challenges faced in the secure software development process are quality and reliability. There are major software cost violations when a software product with errors in its various components is used on the customer’s side. The software warehouse is commonly used as a record keeping repository, which is often needed when adding new features or fixing bugs. Software errors can lead to erroneous and different results. As a result, software programs run late, are canceled, or become unreliable after use. Different social and technical issues are associated with software failure and software defects are the main reasons for deteriorating product quality. In software engineering, the most active research in software domain is defect prediction.This study discusses the bug-fix time forecast model, pre-release release, post-release error and different measurements to predict failures. Predicted results help developers identify and fix potential vulnerabilities, thereby improving software stability and reliability.

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20

Yang, Jie, Chenzhou Ye, and Nianyi Chen. "DMiner-I: A software tool of data mining and its applications." Robotica 20, no. 5 (September 2002): 499–508. http://dx.doi.org/10.1017/s0263574702004307.

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SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

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21

Kessentini, Marouane, and Tim Menzies. "A guest editorial: special issue on search based software engineering and data mining." Automated Software Engineering 24, no. 3 (May 19, 2017): 573–74. http://dx.doi.org/10.1007/s10515-017-0217-2.

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22

Smith, Martin L. "Overcoming the Limitations of Mining Software Systems: Data Export, Secondary Software & Math Programming." Mineral Resources Engineering 07, no. 02 (June 1998): 111–30. http://dx.doi.org/10.1142/s0950609898000134.

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23

Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high.This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories.

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Yang, Fan, and Xiao Dong Cheng. "Software Development in Mining Subsedence Prediction." Advanced Materials Research 760-762 (September 2013): 1967–71. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1967.

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Long-term scientific research and production practice show that there are rules to follow in mining influence, but the amount of measurement data collation and analysis not only cost of manpower, material resources, and calculation is very prone to error. Computer to replace manual calculation, not only convenient, quick, efficient, and adopts automatic generation technology by drawing graphs, is unattainable by manual technology. Mine land reclamation in mining subsidence is expected to software research for mining design, mining area coal mine district design, land compensation, land reclamation and comprehensive utilization technology work provides a scientific basis, promote the process of security coal pillar mining, ensures the mine in each work smoothly, improve the economic benefit of mine, to the national economy and the sustainable development of coal industry itself has important significance.

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Riquelme, J. C., R. Ruiz, D. Rodriguez, and J. S. Aguilar-Ruiz. "Finding Defective Software Modules by Means of Data Mining Techniques." IEEE Latin America Transactions 7, no. 3 (July 2009): 377–82. http://dx.doi.org/10.1109/tla.2009.5336637.

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Pastuchová, Elena, and Štefánia Václavíková. "Cluster Analysis – Data Mining Technique for Discovering Natural Groupings in the Data." Journal of Electrical Engineering 64, no. 2 (March 1, 2013): 128–31. http://dx.doi.org/10.2478/jee-2013-0019.

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Amount of data stored in databases has been growing rapidly. With the technology of pattern recognition and statistical and mathematical techniques sieved across the stored information, data mining helps researchers recognize important facts, relationships, trends, patterns, derogations and anomalies that might otherwise go undetected. One of the major data mining techniques is clustering In this paper some of clustering methods, helpful in many applications, are compared. We assess the suitability of the software that we used for clustering.

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CATALDO, MARCELO, INGO SCHOLTES, and GIUSEPPE VALETTO. "A COMPLEX NETWORKS PERSPECTIVE ON COLLABORATIVE SOFTWARE ENGINEERING." Advances in Complex Systems 17, no. 07n08 (December 2014): 1430001. http://dx.doi.org/10.1142/s0219525914300011.

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Large collaborative software engineering projects are interesting examples for evolving complex systems. The complexity of these systems unfolds both in evolving software structures, as well as in the social dynamics and organization of development teams. Due to the adoption of Open Source practices and the increasing use of online support infrastructures, large-scale data sets covering both the social and technical dimension of collaborative software engineering processes are increasingly becoming available. In the analysis of these data, a growing number of studies employ a network perspective, using methods and abstractions from network science to generate insights about software engineering processes. Featuring a collection of inspiring works in this area, with this topical issue, we intend to give an overview of state-of-the-art research. We hope that this collection of articles will stimulate downstream applications of network-based data mining techniques in empirical software engineering.

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Wu, Xindong, and Xingquan Zhu. "Mining With Noise Knowledge: Error-Aware Data Mining." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 38, no. 4 (July 2008): 917–32. http://dx.doi.org/10.1109/tsmca.2008.923034.

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Yeh, Ruey-Ling, Ching Liu, Ben-Chang Shia, Yu-Ting Cheng, and Ya-Fang Huwang. "Imputing manufacturing material in data mining." Journal of Intelligent Manufacturing 19, no. 1 (November 21, 2007): 109–18. http://dx.doi.org/10.1007/s10845-007-0067-z.

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30

Sydorova, Maryna, Oleg Baybuz, Olha Verba, and Pavlo Pidhornyi. "INFORMATION TECHNOLOGY FOR TRAJECTORY DATA MINING." Science and Innovation 17, no. 3 (June 17, 2021): 78–86. http://dx.doi.org/10.15407/scine17.03.078.

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Introduction. Advanced technologies allow almost continuous tracking and recording the movement of objects inspace and time. Detecting interesting patterns in these data, popular routes, habits, and anomalies in object motion and understanding mobility behaviors are actual tasks in different application areas such as marketing, urban planning, transportation, biology, ecology, etc.Problem Statement. In order to obtain useful information from trajectories of moving objects, it is important to develop and to improve mathematical methods of spatiotemporal analysis and to implement them in highquality modern software.Purpose. The purpose of this research is the development of information technology for trajectory data mining.Materials and Methods. Information technology contains the three main algorithms: revealing key pointsand sequences of interest with the use of density-based trajectories clustering of studied objects; detecting patterns of an object movement based on association rules and hierarchical cluster analysis of its motion trajectories in the time interval of observations, similarity measure of the motion trajectories has been proposed to be calculated on the basis of the DTW method with the use of the modified Haversine formula; new algorithm for revealing permanent routes and detecting groups of similar objects has been developed on the basis of clustering ensemblesof all studied trajectories in time. The clustering parameters are selected with multi-criteria quality evaluation.Results. The modern software that implements the proposed algorithms and provides a convenient interactionwith users and a variety of visualization tools has been created. The developed algorithms and software have beentested in detail on the artificial trajectories of moving objects and applied to analysis of real open databases.Conclusions. The experiments have confirmed the efficiency of the proposed information technology thatmay have a practicable application to trajectory data mining in various fields.

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Hossein Alavi, Amir, and Amir Hossein Gandomi. "A robust data mining approach for formulation of geotechnical engineering systems." Engineering Computations 28, no. 3 (April 5, 2011): 242–74. http://dx.doi.org/10.1108/02644401111118132.

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Ahsan, Syed Nadeem, Muhammad Tanvir Afzal, Safdar Zaman, Christian Gütel, and Franz Wotawa. "MINING EFFORT DATA FROM THE OSS REPOSITORY OF DEVELOPER’S BUG FIX ACTIVITY." Journal of IT in Asia 3, no. 1 (April 20, 2016): 107–28. http://dx.doi.org/10.33736/jita.38.2010.

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During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort. Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs.

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Savaglio, Claudio, and Giancarlo Fortino. "A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing." ACM Transactions on Internet Technology 21, no. 2 (March 3, 2021): 1–22. http://dx.doi.org/10.1145/3402444.

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With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

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Jakhar, Amit Kumar, and Kumar Rajnish. "Software Fault Prediction with Data Mining Techniques by Using Feature Selection Based Models." International Journal on Electrical Engineering and Informatics 10, no. 3 (September 30, 2018): 447–65. http://dx.doi.org/10.15676/ijeei.2018.10.3.3.

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Hu, Wei, and Jia Lin Zhang. "Meshing Analysis on Stress Distribution of Mechanical Structure Based on Digital Mining Technology." Applied Mechanics and Materials 543-547 (March 2014): 1939–42. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1939.

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The data mining technology is one of the most commonly used computer data analysis method. This paper establishes finite difference model of the data mining process using forward first difference, backward first difference and central difference and verifies the feasibility of the algorithm through Matlab software. In order to verify the suitability of the algorithm, the paper takes modeling and meshing of Proe mechanical engineering software for example and establishes the model for the yankee dryer. It achieves the meshing of complex dryer model and gets the better unstructured tetrahedral mesh. It also introduces grid to the computer mechanics simulation software and concludes simulation stress distribution of dryer through the calculation. At last, this paper applies the finite difference data mining algorithms to the research of the development of in local sports single associations. This paper concludes the characteristic curve of the development of local sports federations which realizes the application of computer data mining in the analysis of sports data.

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Zhang, Jia Liang, Jian Guo Yang, Shou Guo Shen, and Han Yan Chen. "Process Optimization of Candy Production Based on Data Mining." Advanced Materials Research 282-283 (July 2011): 662–65. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.662.

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There are complicated correlations between process parameters and quality indicators in candy manufacturing. The objective of this work is to develop an optimization system of candy production process to improve final candy quality and to increase production efficiency. The study is conducted by using an artificial neural network data mining method to obtain optimization knowledge of process parameters from large amount of saved process data. The software platform including data processing, statistic analysis, data mining and graphical display module was developed and the quality forecasting models for typical processing operations were discussed. Experiments indicated that the system can optimize and predict the quality of candy production process effectively.

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Xie, Fei Hong, Zhi Yong Kou, and Yong Mou Zhang. "Prediction and Discuss of Strap Mining Subsidence by Numerical Simulation Analysis and its Engineering Apply." Advanced Materials Research 308-310 (August 2011): 1683–87. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1683.

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In this paper, In order to reasonably determine the mining width and reserves length of strip mining, reliable simulation subsidence due to strip mining under earth，according to the cavity environment of the engineering measure, the protected object's space position and the mining rock strata's circumstance, and the relevant mining subsidence mechanic, calculation model are chosen in order to predicting the designing mining area transform and data sorting after mining. Its function and perform is put into practice for all various aspects of subsidence calculation in visual analysis system of own developed software package. It is applicable to all mining geological conditions and mining methods. Based on the condition of strip and pillar practice of Matigou colliery of Huating Mining Group, It is also applicable to this calculation system to guide successfully mining under river and architectural complex, the accurate estimate forecast had been attained.

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Pal, Sankar K., and Ashish Ghosh. "Soft computing data mining." Information Sciences 163, no. 1-3 (June 2004): 1–3. http://dx.doi.org/10.1016/j.ins.2003.03.012.

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Boersch, Ingo, Uwe Füssel, Christoph Gresch, Christoph Großmann, and Benjamin Hoffmann. "Data mining in resistance spot welding." International Journal of Advanced Manufacturing Technology 99, no. 5-8 (December 12, 2016): 1085–99. http://dx.doi.org/10.1007/s00170-016-9847-y.

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40

Stockton, David John, Riham Ashley Khalil, and Lawrence Manyonge Mukhongo. "Cost model development using virtual manufacturing and data mining: part II—comparison of data mining algorithms." International Journal of Advanced Manufacturing Technology 66, no. 9-12 (September 2, 2012): 1389–96. http://dx.doi.org/10.1007/s00170-012-4416-5.

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Ehrenman, Gayle. "Mining What Others Miss." Mechanical Engineering 127, no. 02 (February 1, 2005): 26–31. http://dx.doi.org/10.1115/1.2005-feb-1.

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This article discusses data mining that draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high-performance computing to discover interesting and previously unknown information in data. More specifically, data mining is the analysis of 10 large data sets to find relationships and patterns that aren’t readily apparent, and to summarize the data in new and useful ways. Data mining technology has enabled earth scientists from NASA to discover changes in the global carbon cycle and climate system, and biologists to map and explore the human genome. Data mining is not restricted solely to vast banks of data with unlimited ways of analyzing it. Manufacturers, such as W.L. Gore (the maker of GoreTex) use commercially available data mining tools to warehouse and analyze their data, and improve their manufacturing process. Gore uses data mining tools from analytic software vendor SAS for statistical modeling in its manufacturing process.

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Gao, Wei Wei, Jian Hua Wang, and Xiao Feng Li. "Design of Intelligent Teaching System Based on Data Mining Technology." Advanced Materials Research 834-836 (October 2013): 998–1001. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.998.

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Data mining technology into the teaching system, the biggest advantage is that the system can gather large amounts of data for analysis , digging out of the course content and teaching strategies presented useful information on the adjustment in order to build content-rich smart teaching platform . This paper mainly made use of data mining techniques to solve the data mining technology is introduced into the system in order to fully improve the system for students and student learning characteristics of the implementation of individualized teaching of intelligence, flexibility in learning mode , the number of users and courses content scalability , research and development of an online learning system . With these results the general software development technology applied to intelligent tutoring system for students to build an adaptive, personalized student-centered learning platform.

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Rezania, Mohammad, Akbar A. Javadi, and Orazio Giustolisi. "An evolutionary‐based data mining technique for assessment of civil engineering systems." Engineering Computations 25, no. 6 (August 22, 2008): 500–517. http://dx.doi.org/10.1108/02644400810891526.

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Janardhanan, Padmavathi, Heena L., and Fathima Sabika. "Effectiveness of Support Vector Machines in Medical Data mining." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 25. http://dx.doi.org/10.24138/jcomss.v11i1.114.

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The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classification. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust and effective classifier for medical data sets.

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Verstak, Alex, Naren Ramakrishnan, Layne T. Watson, Jian He, Clifford A. Shaffer, and Ananth Y. Grama. "Using hierarchical data mining to characterize performance of wireless system configurations." Advances in Engineering Software 65 (November 2013): 66–77. http://dx.doi.org/10.1016/j.advengsoft.2013.05.012.

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Dong, Bo, Matthew M. Lin, and Haesun Park. "Integer Matrix Approximation and Data Mining." Journal of Scientific Computing 75, no. 1 (September 8, 2017): 198–224. http://dx.doi.org/10.1007/s10915-017-0531-7.

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Li, Xiaonan, and Sigurdur Olafsson. "Discovering Dispatching Rules Using Data Mining." Journal of Scheduling 8, no. 6 (December 2005): 515–27. http://dx.doi.org/10.1007/s10951-005-4781-0.

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Zhang, Du, Quoc Luan Ha, and Meiliu Lu. "Mining California vital statistical data." International Journal of Computer Applications in Technology 27, no. 4 (2006): 281. http://dx.doi.org/10.1504/ijcat.2006.011999.

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SUBRAMANYAM, R. B. V., and A. GOSWAMI. "A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 06 (December 2005): 633–52. http://dx.doi.org/10.1142/s0218488505003722.

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In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

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Kusiak, Andrew, and Matthew Smith. "Data mining in design of products and production systems." Annual Reviews in Control 31, no. 1 (January 2007): 147–56. http://dx.doi.org/10.1016/j.arcontrol.2007.03.003.

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