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Articles de revues sur le sujet « Data Mining and Knowledge DiscoveryID »

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Auteur : Grafiati
Publié le 14 mars 2023

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1

Bhojani, Shital Hitesh. « Geospatial Data Mining Techniques : Knowledge Discovery in Agricultural ». Indian Journal of Applied Research 3, no 1 (1 octobre 2011) : 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.

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Abdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan et Haitham Ali Hussain. « Application of Data Mining and Knowledge Discovery in Medical Databases ». Webology 19, no 1 (20 janvier 2022) : 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.

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While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.
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KAWANO, Hiroyuki. « Knowledge Discovery and Data Mining ». Journal of Japan Society for Fuzzy Theory and Systems 9, no 6 (1997) : 851–60. http://dx.doi.org/10.3156/jfuzzy.9.6_851.

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IWASAKI, Manabu. « Data Mining and Knowledge Discovery ». Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 26, no 1 (1999) : 46–58. http://dx.doi.org/10.2333/jbhmk.26.46.

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Chikalkar, Siddharth Nandakumar. « Knowledge Discovery and Data Mining ». International Journal for Research in Applied Science and Engineering Technology 8, no 10 (31 octobre 2020) : 874–76. http://dx.doi.org/10.22214/ijraset.2020.32045.

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Brodley, Carla, Terran Lane et Timothy Stough. « Knowledge Discovery and Data Mining ». American Scientist 87, no 1 (1999) : 54. http://dx.doi.org/10.1511/1999.16.807.

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Lee, Hing-Yan, Hongjun Lu et Hiroshi Motoda. « Knowledge discovery and data mining ». Knowledge-Based Systems 10, no 7 (mai 1998) : 401–2. http://dx.doi.org/10.1016/s0950-7051(98)00033-1.

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Clancy, Thomas Roy, et Lillee Gelinas. « Knowledge Discovery and Data Mining ». JONA : The Journal of Nursing Administration 46, no 9 (septembre 2016) : 422–24. http://dx.doi.org/10.1097/nna.0000000000000369.

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Ruzgas, Tomas, Kristina Jakubėlienė et Aistė Buivytė. « Big Data Mining and Knowledge Discovery ». Journal of Communications Technology, Electronics and Computer Science 9 (27 décembre 2016) : 5. http://dx.doi.org/10.22385/jctecs.v9i0.134.

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The article dealt with exploration methods and tools for big data. It identifies the challenges encountered in the analysis of big data. Defined notion of big data. describe the technology for big data analysis. Article provides an overview of tools which are designed for big data analytics.
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Gupta, Aman. « Data Mining to Discovery of Knowledge ». International Journal for Research in Applied Science and Engineering Technology 10, no 6 (30 juin 2022) : 309–11. http://dx.doi.org/10.22214/ijraset.2022.43643.

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Chhabra, Indu, et Gunmala Suri. « Knowledge Discovery for Scalable Data Mining ». ICST Transactions on Scalable Information Systems 6, no 21 (10 juin 2019) : 158527. http://dx.doi.org/10.4108/eai.19-3-2019.158527.

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Cios, K. J., W. Pedrycz et R. M. Swiniarsk. « Data Mining Methods for Knowledge Discovery ». IEEE Transactions on Neural Networks 9, no 6 (novembre 1998) : 1533–34. http://dx.doi.org/10.1109/tnn.1998.728406.

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Petrushin,, Valery A. « Multimedia Data Mining and Knowledge Discovery ». Journal of Electronic Imaging 17, no 4 (1 janvier 2007) : 049901. http://dx.doi.org/10.1117/1.3040688.

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Al Hasan, Mohammad, Jun Huan, Jake Chen et Mohammed J. Zaki. « Biological Knowledge Discovery and Data Mining ». Scientific Programming 20, no 1 (2012) : 1–2. http://dx.doi.org/10.1155/2012/252681.

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., D. M. Kulkarni. « USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING ». International Journal of Research in Engineering and Technology 03, no 01 (25 janvier 2014) : 24–29. http://dx.doi.org/10.15623/ijret.2014.0301005.

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Zimmermann, H. J. « KNOWLEDGE MANAGEMENT, KNOWLEDGE DISCOVERY, AND DYNAMIC INTELLIGENT DATA MINING ». Cybernetics and Systems 37, no 6 (septembre 2006) : 509–31. http://dx.doi.org/10.1080/01969720600734412.

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Ziegel, Eric R., Usama M. Fayyad, Gregory Piatetski-Shapiro, Padhraic Smyth et Ramasamy Uthurusamy. « Advances in Knowledge Discovery and Data Mining ». Technometrics 40, no 1 (février 1998) : 83. http://dx.doi.org/10.2307/1271414.

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Helma, C., E. Gottmann et S. Kramer. « Knowledge discovery and data mining in toxicology ». Statistical Methods in Medical Research 9, no 4 (1 août 2000) : 329–58. http://dx.doi.org/10.1191/096228000701555190.

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Fayyad, Usama, et Ramasamy Uthurusamy. « Data mining and knowledge discovery in databases ». Communications of the ACM 39, no 11 (novembre 1996) : 24–26. http://dx.doi.org/10.1145/240455.240463.

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Helma, Christoph, Eva Gottmann et Stefan Kramer. « Knowledge discovery and data mining in toxicology ». Statistical Methods in Medical Research 9, no 4 (août 2000) : 329–58. http://dx.doi.org/10.1177/096228020000900403.

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21

Berhe, Zenawi Haileslassie, et Vinod Kumar. « Knowledge Discovery and Data Mining Review Papers ». International Journal of Engineering Trends and Technology 67, no 4 (25 avril 2019) : 1–3. http://dx.doi.org/10.14445/22315381/ijett-v67i4p201.

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Dasarathy, Belur V. « Information fusion, data mining, and knowledge discovery ». Information Fusion 4, no 1 (mars 2003) : 1. http://dx.doi.org/10.1016/s1566-2535(02)00122-7.

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Liu, Qian, Fei Xiao et Zhiye Zhao. « Grouting knowledge discovery based on data mining ». Tunnelling and Underground Space Technology 95 (janvier 2020) : 103093. http://dx.doi.org/10.1016/j.tust.2019.103093.

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Rikhi, Nainja. « Data Mining and Knowledge Discovery in Database ». International Journal of Engineering Trends and Technology 23, no 2 (25 mai 2015) : 64–70. http://dx.doi.org/10.14445/22315381/ijett-v23p213.

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Verma, Tarika, et Dr Chhavi Rana. « Data Mining Techniques for the Knowledge Discovery ». International Journal of Engineering and Technology 9, no 3S (17 juillet 2017) : 351–54. http://dx.doi.org/10.21817/ijet/2017/v9i3/170903s054.

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26

Exner (Little Bear), Frank. « Advances in knowledge discovery and data mining ». Journal of the American Society for Information Science 49, no 4 (1998) : 386–87. http://dx.doi.org/10.1002/(sici)1097-4571(19980401)49:4<386 ::aid-asi14>3.0.co;2-j.

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Exner (Little Bear), Frank. « Advances in knowledge discovery and data mining ». Journal of the American Society for Information Science 49, no 4 (1 avril 1998) : 386–87. http://dx.doi.org/10.1002/(sici)1097-4571(19980401)49:4<386 ::aid-asi14>3.3.co;2-a.

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Pedrycz, Witold. « Introducing WIREs Data Mining and Knowledge Discovery ». WIREs Data Mining and Knowledge Discovery 1, no 1 (janvier 2011) : 1. http://dx.doi.org/10.1002/widm.17.

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Hegland, Markus. « Data mining techniques ». Acta Numerica 10 (mai 2001) : 313–55. http://dx.doi.org/10.1017/s0962492901000058.

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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.
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CHEN, ZHENGXIN. « FROM DATA MINING TO BEHAVIOR MINING ». International Journal of Information Technology & ; Decision Making 05, no 04 (décembre 2006) : 703–11. http://dx.doi.org/10.1142/s0219622006002271.

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Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of complex data (as exemplified by graph mining, text mining, etc.). By incorporating the relationships of the data along with the data itself (rather than focusing on the data alone), complex data injects semantics into the mining process, thus enhancing the potential of making better contribution to knowledge economy. Since the relationships between the data reveal certain behavioral aspects underlying the plain data, this shift of mining from simple data to complex data signals a fundamental change to a new stage in the research and practice of knowledge discovery, which can be termed as behavior mining. Behavior mining also has the potential of unifying some other recent activities in data mining. We discuss important aspects on behavior mining, and discuss its implications for the future of data mining.
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31

Hao, Wu. « On Business-Oriented Knowledge Discovery and Data Mining ». Advanced Materials Research 760-762 (septembre 2013) : 2267–71. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2267.

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This paper will discuss issues in data mining and business processes including Marketing, Finance and Health. In turn, the use of KDD in the complex real-world databases in business and government will push the IT researchers to identify and solve cutting-edge problems in KDD modelling, techniques and processes. From IT perspectives, some issues in economic sciences consist of business modelling and mining, aberrant behavior detection, and health economics. Some issues in KDD include data mining for complex data structures and complex modelling. These novel strategies will be integrated to build a one-stop KDD system.
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32

CAO, LONGBING, et CHENGQI ZHANG. « THE EVOLUTION OF KDD : TOWARDS DOMAIN-DRIVEN DATA MINING ». International Journal of Pattern Recognition and Artificial Intelligence 21, no 04 (juin 2007) : 677–92. http://dx.doi.org/10.1142/s0218001407005612.

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Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, real-world applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligence is highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data.
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HO, TUBAO, TRONGDUNG NGUYEN, DUCDUNG NGUYEN et SAORI KAWASAKI. « VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING ». International Journal on Artificial Intelligence Tools 10, no 04 (décembre 2001) : 691–713. http://dx.doi.org/10.1142/s0218213001000726.

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The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.
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Pan, Zhiwen, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai et Jun Zhang. « Knowledge discovery in sociological databases ». International Journal of Crowd Science 3, no 3 (2 septembre 2019) : 315–32. http://dx.doi.org/10.1108/ijcs-09-2019-0023.

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Purpose The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets. Design/methodology/approach The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis. Findings According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other. Originality/value By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.
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Lee, Jun-Wook, et Yong-Joon Lee. « A Knowledge Discovery Framework for Spatiotemporal Data Mining ». Journal of Information Processing Systems 2, no 2 (30 juin 2006) : 124–29. http://dx.doi.org/10.3745/jips.2006.2.2.124.

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Vityaev, E. E., et B. Y. Kovalerchuk. « Relational methodology for data mining and knowledge discovery ». Intelligent Data Analysis 12, no 2 (16 avril 2008) : 189–210. http://dx.doi.org/10.3233/ida-2008-12204.

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Foody, Giles M. « Uncertainty, knowledge discovery and data mining in GIS ». Progress in Physical Geography : Earth and Environment 27, no 1 (mars 2003) : 113–21. http://dx.doi.org/10.1191/0309133303pp345pr.

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Sieber, Joan E. « Data Mining : Knowledge Discovery for Human Research Ethics ». Journal of Empirical Research on Human Research Ethics 3, no 3 (septembre 2008) : 1–2. http://dx.doi.org/10.1525/jer.2008.3.3.1.

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Helma, C. « Data Mining and Knowledge Discovery in Predictive Toxicology ». SAR and QSAR in Environmental Research 15, no 5-6 (octobre 2004) : 367–83. http://dx.doi.org/10.1080/10629360412331297407.

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Zhao, Jun, Witold Pedrycz et Sabrina Senatore. « Data Mining and Knowledge Discovery in Industrial Engineering ». Mathematical Problems in Engineering 2013 (2013) : 1–2. http://dx.doi.org/10.1155/2013/790160.

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BRUSIC, VLADIMIR, et JOHN ZELEZNIKOW. « Knowledge discovery and data mining in biological databases ». Knowledge Engineering Review 14, no 3 (septembre 1999) : 257–77. http://dx.doi.org/10.1017/s0269888999003069.

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Eide, Åge, Robert Johansson, Thomas Lindblad et Clark S. Lindsey. « Data mining and neural networks for knowledge discovery ». Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment 389, no 1-2 (avril 1997) : 251–54. http://dx.doi.org/10.1016/s0168-9002(97)00145-9.

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Fan, Jianhua, et Deyi Li. « An overview of data mining and knowledge discovery ». Journal of Computer Science and Technology 13, no 4 (juillet 1998) : 348–68. http://dx.doi.org/10.1007/bf02946624.

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R, Ankita. « Knowledge Discovery using Various Multimedia Data Mining Technique ». International Journal on Recent and Innovation Trends in Computing and Communication 3, no 3 (2015) : 1138–41. http://dx.doi.org/10.17762/ijritcc2321-8169.150354.

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Babovic, Vladan. « Data Mining and Knowledge Discovery in Sediment Transport ». Computer-Aided Civil and Infrastructure Engineering 15, no 5 (septembre 2000) : 383–89. http://dx.doi.org/10.1111/0885-9507.00202.

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Wang, Shuliang, et Hanning Yuan. « Spatial Data Mining ». International Journal of Data Warehousing and Mining 10, no 4 (octobre 2014) : 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.

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Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.
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Mariscal, Gonzalo, Óscar Marbán et Covadonga Fernández. « A survey of data mining and knowledge discovery process models and methodologies ». Knowledge Engineering Review 25, no 2 (juin 2010) : 137–66. http://dx.doi.org/10.1017/s0269888910000032.

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AbstractUp to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process namedrefined data mining processfor developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.
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Park, Myonghwa, Jeong Sook Park, Chong Nam Kim, Kyung Min Park et Young Sook Kwon. « Knowledge Discovery in Nursing Minimum Data Set Using Data Mining ». Journal of Korean Academy of Nursing 36, no 4 (2006) : 652. http://dx.doi.org/10.4040/jkan.2006.36.4.652.

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Feyyad, U. M. « Data mining and knowledge discovery : making sense out of data ». IEEE Expert 11, no 5 (octobre 1996) : 20–25. http://dx.doi.org/10.1109/64.539013.

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Alonso, Fernando, Loïc Martínez, Aurora Pérez et Juan P. Valente. « Cooperation between expert knowledge and data mining discovered knowledge : Lessons learned ». Expert Systems with Applications 39, no 8 (juin 2012) : 7524–35. http://dx.doi.org/10.1016/j.eswa.2012.01.133.

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