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Thematische Bibliographien / Data mining / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Data mining“

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Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2024

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1

PYLYPIUK, Tetiana, und Viktor SHCHYRBA. „DATA MINING METHODS“. Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 29 (14.12.2023): 7–10. http://dx.doi.org/10.32626/2307-4507.2023-29.7-10.

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Research is devoted to Data Mining methods. A comparison of classical and mathematical and statistical methods of data analysis was made. One of the variants of correlation analysis method for intelligent data analysis is proposed and described in an argumentative manner. The question of applying different methodologies for Data Mining is actual. Classically, the following methods of knowledge discovery and analysis are offered in Data Mining: classification; regression; forecasting time sequences (series); clustering; association. As mathematical and statistical methods of analysis in applied research, the most of authors offer such methods as: statistical hypothesis testing, regression models construction and research. Since most real models are not amenable to analysis using classical methods, including regression analysis, the authors propose to use correlational analysis method in Data Mining.

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2

Shah Neha K, Shah Neha K. „Introduction of Data mining and an Analysis of Data mining Techniques“. Indian Journal of Applied Research 3, Nr. 5 (01.10.2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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3

Rakholiya, Kalpesh R., und Dr Dhaval Kathiriya. „Data Mining for Moving Object Data“. Indian Journal of Applied Research 2, Nr. 3 (01.10.2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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4

Chomboon, K., N. Kaoungku, K. Kerdprasop und N. Kerdprasop. „Data Mining in Semantic Web Data“. International Journal of Computer Theory and Engineering 6, Nr. 6 (Dezember 2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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5

Загороднюк, П. А. „Data mining in Go“. Vestnik of Russian New University. Series «Complex systems: models, analysis, management», Nr. 4 (10.01.2022): 161–66. http://dx.doi.org/10.18137/rnu.v9187.21.04.p.161.

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Целью данной статьи является оценка языка программирования Go как инструмента для реализации методов data mining. Для этого проводится анализ задачи классификации и метода k-ближайших соседей, затем предлагается способ программирования данного метода и организации процесс управления и подготовки исходных данных. В заключение на основе проведенной работы делается вывод, насколько Go подходит для решения подобных задач, и есть ли потенциал для реализации остальных методов. The purpose of this article is to evaluate the Go programming language as a tool for implementing data mining methods. To do this, an analysis of the classification problem and the k-nearest neighbors’ algorithm is carried out, then a method is proposed for how this method can be programmed and the process of managing and preparing the initial data can be organized. In conclusion, based on the work carried out, it sums up how well Go is suitable for solving such problems and whether there is potential for the implementation of other methods.

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AVeselý. „Neural networks in data mining“. Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (02.03.2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen’s self-organizing maps are discussed. Kohonen’s self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.

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7

M., Inbavalli. „An Intelligent Agent based Mining Techniques for Distributed Data Mining“. Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31.03.2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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8

Raval, Hitesh R., und Dr Vikram Kaushik. „Data Mining: Performance Tuning Of Temporal Data Mining Based On Frequent Inter-Transaction Itemsets Discovery“. International Journal of Scientific Research 3, Nr. 2 (01.06.2012): 78–82. http://dx.doi.org/10.15373/22778179/feb2014/25.

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9

Stoffel, Kilian. „Web + Data Mining = Web Mining“. HMD Praxis der Wirtschaftsinformatik 46, Nr. 4 (August 2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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10

Tsuta, Mizuki. „Data Mining“. Nippon Shokuhin Kagaku Kogaku Kaishi 64, Nr. 6 (2017): 334–35. http://dx.doi.org/10.3136/nskkk.64.334.

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11

Rossini, Luiz Amelio Sodaite, Renan Ricardo de Polli Silva, Eder Carlos Salazar Sotto und Liriane Soares De Araújo. „DATA MINING“. Revista Interface Tecnológica 15, Nr. 2 (30.12.2018): 50–59. http://dx.doi.org/10.31510/infa.v15i2.486.

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A Mineração de Dados (Data Mining) deve ser entendida como um conjunto de esforços empregados para a descoberta de padrões de acordo com bases de dados. Dessa maneira, há condições de gerar conhecimento útil para a tomada de decisões, através de algoritmos computacionais que recebem fatos do mundo real (entrada) e devolvem um padrão de comportamento (saída), expresso como modelagem de um perfil. Sendo assim, o objetivo deste artigo é definir a Mineração de Dados e os conceitos inerentes a ela, bem como elencar algumas ferramentas utilizadas para extração de conhecimento a partir dos dados. A metodologia do trabalho consiste em levantamento bibliográfico. Espera-se como resultado demonstrar a imprescindibilidade da Mineração de Dados no apoio à decisão nas organizações e contribuir para a produção científica e acadêmica.

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12

Nazem, Sufi M., und Bongsik Shin. „Data Mining“. Journal of Database Management 10, Nr. 1 (Januar 1999): 39–42. http://dx.doi.org/10.4018/jdm.1999010104.

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13

Sharma, Anubhav. „Data Mining“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. 4 (30.04.2021): 953–56. http://dx.doi.org/10.22214/ijraset.2021.33790.

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14

Ziegel, Eric R., und Bhavani Thuraisingham. „Data Mining“. Technometrics 42, Nr. 3 (August 2000): 327. http://dx.doi.org/10.2307/1271124.

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15

Alkadi, Ihssan. „Data Mining“. Review of Business Information Systems (RBIS) 12, Nr. 1 (01.01.2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has led to the development of relational database systems, data modeling tools, and indexing and data organization techniques. In relational database systems data are stored in relational tables. In addition, users can get convenient and flexible access to data through query languages, optimized query processing, user interfaces and transaction management and optimized methods for On-Line Transaction Processing (OLTP). The abundant data, which needs powerful data analysis tools, has been described as a data rich but information poor situation. The fast-growing, tremendous amount of data, collected and stored in large and numerous databases. Humans can not analyze these large amounts of data. So we need powerful tools to analyze this large amount of data. As a result, data collected in large databases become data tombs. These are data archives that are seldom visited. So, important decisions are often not made based on the information-rich data stored in databases rather based on a decision maker's intuition. This is because the decision maker does not have the tools to extract the valuable knowledge embedded in the vast amounts of data. Data mining tools which perform data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research. So data mining tools will turn data tombs into golden nuggets of knowledge.

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16

Kemp, Freda. „Data Mining“. Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, Nr. 1 (Februar 2004): 190–91. http://dx.doi.org/10.1111/j.1467-985x.2004.298_9.x.

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17

Lynch, Patrick K. „Data Mining“. Biomedical Instrumentation & Technology 43, Nr. 1 (01.01.2009): 8. http://dx.doi.org/10.2345/0899-8205-43.1.8.

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18

Hand, David J. „Data Mining“. Social Science Computer Review 18, Nr. 4 (November 2000): 442–49. http://dx.doi.org/10.1177/089443930001800407.

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19

Yongjian Fu. „Data mining“. IEEE Potentials 16, Nr. 4 (1997): 18–20. http://dx.doi.org/10.1109/45.624335.

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20

O'Shea, Donald C. „Data Mining“. Optical Engineering 37, Nr. 11 (01.11.1998): 2869. http://dx.doi.org/10.1117/1.601979.

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21

Olaru, C., und L. Wehenkel. „Data mining“. IEEE Computer Applications in Power 12, Nr. 3 (Juli 1999): 19–25. http://dx.doi.org/10.1109/67.773801.

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22

Berzal, Fernando, und Nicolfás Matín. „Data mining“. ACM SIGMOD Record 31, Nr. 2 (Juni 2002): 66–68. http://dx.doi.org/10.1145/565117.565130.

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23

Witten, Ian H., und Eibe Frank. „Data mining“. ACM SIGMOD Record 31, Nr. 1 (März 2002): 76–77. http://dx.doi.org/10.1145/507338.507355.

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24

Ngo, Terry. „Data mining“. ACM SIGSOFT Software Engineering Notes 36, Nr. 5 (30.09.2011): 51–52. http://dx.doi.org/10.1145/2020976.2021004.

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25

Hong, Se June. „Data mining“. Future Generation Computer Systems 13, Nr. 2-3 (November 1997): 95–97. http://dx.doi.org/10.1016/s0167-739x(97)00014-9.

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26

Ozimek, John. „Data Mining“. Journal of Database Marketing & Customer Strategy Management 10, Nr. 3 (April 2003): 280–81. http://dx.doi.org/10.1057/palgrave.jdm.3240117.

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27

METCALF, A. „DATA MINING“. American Speech 75, Nr. 3 (01.09.2000): 237–39. http://dx.doi.org/10.1215/00031283-75-3-237.

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28

Meyer, Matthias. „Data mining“. WIRTSCHAFTSINFORMATIK 48, Nr. 6 (Dezember 2006): 454–55. http://dx.doi.org/10.1007/s11576-006-0106-y.

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29

Bissantz, Nicolas, und Jürgen Hagedorn. „Data Mining“. Business & Information Systems Engineering 1, Nr. 1 (14.12.2008): 118–22. http://dx.doi.org/10.1007/s12599-008-0005-4.

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30

Cupples, L. Adrienne, Julia Bailey, Kevin C. Cartier, Catherine T. Falk, Kuang-Yu Liu, Yuanqing Ye, Robert Yu, Heping Zhang und Hongyu Zhao. „Data mining“. Genetic Epidemiology 29, S1 (2005): S103—S109. http://dx.doi.org/10.1002/gepi.20117.

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31

Benoît, Gerald. „Data mining“. Annual Review of Information Science and Technology 36, Nr. 1 (01.02.2005): 265–310. http://dx.doi.org/10.1002/aris.1440360107.

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32

Atif, Mohammad. „Data mining“. International Journal of Communication and Information Technology 3, Nr. 1 (01.01.2022): 37–40. http://dx.doi.org/10.33545/2707661x.2022.v3.i1a.44.

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33

Trivedi, Nripesh. „Data mining“. International Journal of Scientific Research and Management (IJSRM) 12, Nr. 03 (21.03.2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in machine learning. Social networking is about finding patterns in user behaviour and user engagement [5][6].

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34

Rastogi, Mohit. „Spatial data mining features between general data mining“. South Asian Journal of Marketing & Management Research 11, Nr. 11 (2021): 96–101. http://dx.doi.org/10.5958/2249-877x.2021.00116.8.

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35

Bathla, Gourav, Himanshu Aggarwal und Rinkle Rani. „Migrating From Data Mining to Big Data Mining“. International Journal of Engineering & Technology 7, Nr. 3.4 (25.06.2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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36

Zvyagin, L. S. „DATA MINING: BIG DATA AND DATA SCIENCE“. SOFT MEASUREMENTS AND COMPUTING 5, Nr. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize information about the markets in which they operate, which allows them to get a large amount of profit and benefits.

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37

Kriegel, Hans-Peter. „Data Science/Data Mining“. Digitale Welt 3, Nr. 1 (11.12.2018): 7–8. http://dx.doi.org/10.1007/s42354-019-0141-7.

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38

W, Kiehn,. „From Big Data to Data Mining Von Big Data zu Data Mining“. GIS Business 11, Nr. 6 (04.12.2016): 18–20. http://dx.doi.org/10.26643/gis.v11i6.5294.

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39

Maheswari, R. Uma, S. Saravana Mahesan, Dr Tamilarasan und A. K. Subramani. „Role of Data Mining in CRM“. International Journal of Engineering Research 3, Nr. 2 (01.02.2014): 75–78. http://dx.doi.org/10.17950/ijer/v3s2/208.

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40

Anshu, Anshu. „Review Paper on Data Mining TechniquesandApplications“. International Journal of Innovative Research in Computer Science & Technology 7, Nr. 2 (März 2019): 22–26. http://dx.doi.org/10.21276/ijircst.2019.7.2.4.

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41

Sherdiwala, Kainaz Bomi. „Data Mining Techniques in Stock Market“. Indian Journal of Applied Research 4, Nr. 8 (01.10.2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.

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42

Zodage, Kalyani Shahaji, Puja Sarage, Trupti Sudrik und Rashmi Sonawane. „Health Prediction System Using Data Mining“. Journal of Advances and Scholarly Researches in Allied Education 15, Nr. 2 (01.04.2018): 696–99. http://dx.doi.org/10.29070/15/56997.

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43

Pandey, Sachin. „Multilevel Association Rules in Data Mining“. Journal of Advances and Scholarly Researches in Allied Education 15, Nr. 5 (01.07.2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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44

S. Murali, S. Murali, C. B. Selvalakshmi C. B. Selvalakshmi, S. Padmadevi S. Padmadevi und P. N. Karthikayan P. N. Karthikayan. „Data Mining Patters in Grid Computing“. International Journal of Scientific Research 2, Nr. 3 (01.06.2012): 137–38. http://dx.doi.org/10.15373/22778179/mar2013/43.

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45

Hashmi, Faiz. „Elementary approach towards Biological Data Mining“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (31.12.2017): 1109–14. http://dx.doi.org/10.31142/ijtsrd7198.

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46

Kumar, Raj. „Data Mining in Education: A Review“. International Journal Of Mechanical Engineering And Information Technology 05, Nr. 01 (26.01.2017): 1843–45. http://dx.doi.org/10.18535/ijmeit/v5i1.02.

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47

S, Gowtham, und Karuppusamy S. „Review of Data Mining Classification Techniques“. Bonfring International Journal of Software Engineering and Soft Computing 9, Nr. 2 (30.04.2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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48

S, Pramod. „Modified Approach for Online Data Mining“. International Journal of Engineering and Technology 2, Nr. 6 (2010): 533–36. http://dx.doi.org/10.7763/ijet.2010.v2.177.

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49

Yasuda, Akio. „Reviewing "Text Mining": Textual Data Mining“. IEEJ Transactions on Electronics, Information and Systems 125, Nr. 5 (2005): 682–89. http://dx.doi.org/10.1541/ieejeiss.125.682.

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50

CHEN, ZHENGXIN. „FROM DATA MINING TO BEHAVIOR MINING“. International Journal of Information Technology & Decision Making 05, Nr. 04 (Dezember 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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