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Introduction Data mining Data mining is an interdisciplinary field that combines artificial intelligence, database management, data visualization, machine learning, mathematic algorithms, and statistics

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Proc. 19th World Congress of Soil Science 2010 Moran , C. J. & Bui , E. N., 2002. Spatial data mining for

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The study of respiratory forms a major application and publication in the medical field. It characterizes the abnormalities in the breathing pattern, which assists in selecting the appropriate treatment methods. In some cases, respiratory characteristics unravel and point out potential diseases. A medical team gathered data from randomly selected recruits. A huge dataset was prepared, which capture the volume and velocity of the breathed air from the target recruits. This paper presents the results of carrying out some of the signal processing, dimension reduction, and data mining techniques over this dataset. In particular, convolution filter, singular value decomposition and density-based spatial clustering of applications with noise were applied. Inhaling behaviors have been categorized into nine groups but with stochastic noise. Some of the groups are big enough and distinguishable to evaluate with the use of eight types of inhalers the model for velocity versus volume of inhaling. Results will be considered by the medical team for choosing the appropriate inhaler out of five types of inhalers appropriate for each group.

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Abstract  

Based on the Linssi database and UniSampo/Shaman software, an automated analysis platform has been setup for the analysis of large amounts of gamma-spectra from the primary coolant monitoring systems of a CANDU reactor. Thus, a database inventory of gaseous and volatile fission products in the primary coolant of a CANDU reactor has been established. This database is comprised of 15,000 spectra of radioisotope analysis records. Records from the database inventory were retrieved by a specifically designed data-mining module and subjected to further analysis. Results from the analysis were subsequently used to identify the reactor coolant half-life of 135Xe and 133Xe, as well as the correlations of 135Xe and 88Kr activities.

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Scientometrics
Authors: Cristina Faba-Pérez, Vicente Guerrero-Bote, and Félix De Moya-Anegón
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[6] Larose D. T. , Larose C. D. ( 2015 ), Data mining and predictive analytics . John Wiley & Sons [7

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. , Mihálydeák T. Rough clustering generated by correlation clustering , In: Ciucci D., Inuiguchi M., Yao Y., Ślęzak D., Wang G. (Eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Computer Science , Vol. 8170

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2009 36 3355 3366 Burges, C. J. C. (1998): A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining

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Pollack Periodica
Authors: Dávid Nagy, Tamás Mihálydeák, and László Aszalós

– 368 . 10.1016/j.physa.2005.08.008 [12] Aszalos L. , Mihálydeák T. Rough clustering generated by correlation clustering , In: Ciucci D. , Inuiguchi M. , Yao Y. , Ślęzak D. , Wang G. (Eds) Rough Sets, Fuzzy Sets, Data Mining, and

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Summary  

The paper focuses on the Top 500 foreign investment corporations (FICs) in China, by conducting data mining and system searching on the data-base of patent from the State Intellectual Property Office of the People's Republic of China (SIPO). Structure of patent applications, industrial distribution of patent applications, monopolistic tendency, technological innovation of Chinese companies and directions of foreign investment are studied.

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