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Journal articles on the topic 'Pre-processing data'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Kavitha, M., and M. Kasthuri. "Enco – Standardization Data Pre-Processing Technique in Autism Spectrum Disorder Detection." Indian Journal Of Science And Technology 16, no. 45 (2023): 4156–63. http://dx.doi.org/10.17485/ijst/v16i45.1781.

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Mashuri, Mashuri, Albertus Eka Putra Haryanto, Yola Argatha Manik, Dinar Sukma Dewi, and Tegar Primadana Putra. "Dashboard Pre-Processing Data (DPD) as Data Analysis System with Technological Innovation to Perform Pre-Processing Quantitative Data." IPTEK The Journal of Engineering 9, no. 1 (2023): 35. http://dx.doi.org/10.12962/j23378557.v9i1.a13050.

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Jarolímek, Jan, Jan Pavlík, Jana Kholova, and Swarna Ronanki. "Data Pre-processing for Agricultural Simulations." Agris on-line Papers in Economics and Informatics 11, no. 01 (2019): 49–53. http://dx.doi.org/10.7160/aol.2019.110105.

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4

S, Vishesh, Manu Srinath, Akshatha C. Kumar, and Nandan A.S. "Data Warehousing Architecture and Pre-Processing." IJARCCE 6, no. 5 (2017): 13–18. http://dx.doi.org/10.17148/ijarcce.2017.6503.

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5

Wei, Bo, Kai Li, Chengwen Luo, Weitao Xu, Jin Zhang, and Kuan Zhang. "No Need of Data Pre-processing." ACM Transactions on Internet of Things 2, no. 4 (2021): 1–26. http://dx.doi.org/10.1145/3467980.

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Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and

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6

Bilalli, Besim, Alberto Abelló, Tomàs Aluja-Banet, and Robert Wrembel. "Intelligent assistance for data pre-processing." Computer Standards & Interfaces 57 (March 2018): 101–9. http://dx.doi.org/10.1016/j.csi.2017.05.004.

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Garcia de la Nava, J., S. van Hijum, and O. Trelles. "PreP: gene expression data pre-processing." Bioinformatics 19, no. 17 (2003): 2328–29. http://dx.doi.org/10.1093/bioinformatics/btg318.

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8

Ashwini Tuppad and Shantala Devi Patil. "Data Pre-processing Issues in Medical Data Classification." Journal of Advanced Zoology 44, S6 (2023): 1079–84. http://dx.doi.org/10.17762/jaz.v44is6.2361.

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With digitalization of data and the rise of World Wide Web, access to information has been very easy and affordable. Especially the Web and the Internet have boosted research activities by facilitating access to large, publicly available medical datasets under open access scheme. These developments have resulted in explosive amounts of data being generated varying in volume, variety and velocity thus referred to as big data. Availability of such medical big data has catalyzed the research in medical predictive analytics. However, the true value of such data can be derived only after subjecting

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9

Martens, Harald, Martin Høy, Barry M. Wise, Rasmus Bro, and Per B. Brockhoff. "Pre-whitening of data by covariance-weighted pre-processing." Journal of Chemometrics 17, no. 3 (2003): 153–65. http://dx.doi.org/10.1002/cem.780.

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K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-p

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Novo-Lourés, María, Yeray Lage, Reyes Pavón, Rosalía Laza, David Ruano-Ordás, and José Ramón Méndez. "Improving Pipelining Tools for Pre-processing Data." International Journal of Interactive Multimedia and Artificial Intelligence In Press, In Press (2021): 1. http://dx.doi.org/10.9781/ijimai.2021.10.004.

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12

Hu, Li Li, Sheng Suo Niu, and Zhi Rui Liang. "WAMS/PMU Data Pre-Processing and Compression." Advanced Materials Research 986-987 (July 2014): 1700–1703. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1700.

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With the Wide Area Measurement applications, a large number of experimental PMU data was generated. In order to analyze, transmit and apply the data efficiently, by understanding the characteristics of the PMU data in this paper, pressing the data with the Waveform difference method, then using Huffman algorithm to compress the data. Compare the data pre-processing before and after, compression ratio has been further improved.

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13

Gheshlaghi, F., and J. C. Santamarina. "Data Pre‐Processing in Cross‐Hole Geotomography." Journal of Environmental and Engineering Geophysics 3, no. 1 (1998): 41–47. http://dx.doi.org/10.4133/jeeg3.1.41.

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14

Yan, Xiaowei, Chengqi Zhang, and Shichao Zhang. "Toward databases mining: Pre-processing collected data." Applied Artificial Intelligence 17, no. 5-6 (2003): 545–61. http://dx.doi.org/10.1080/713827171.

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15

Moustakidis, Serafeim, Athanasios Anagnostis, Apostolos Chondronasios, Patrik Karlsson, and Kostas Hrissagis. "Excitation-invariant pre-processing of thermographic data." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4 (2018): 435–46. http://dx.doi.org/10.1177/1748006x18770888.

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There is a large number of industries that make extensive use of composite materials in their respective sectors. This rise in composites’ use has necessitated the development of new non-destructive inspection techniques that focus on manufacturing quality assurance, as well as in-service damage testing. Active infrared thermography is now a popular nondestructive testing method for detecting defects in composite structures. Non-uniform emissivity, uneven heating of the test surface, and variation in thermal properties of the test material are some of the crucial factors in experimental thermo

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16

Sendorek, Dorota H., Emilie Lalonde, Cindy Q. Yao, Veronica Y. Sabelnykova, Robert G. Bristow, and Paul C. Boutros. "NanoStringNormCNV: pre-processing of NanoString CNV data." Bioinformatics 34, no. 6 (2017): 1034–36. http://dx.doi.org/10.1093/bioinformatics/btx707.

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17

Franco, Annalisa, Davide Maltoni, and Loris Nanni. "Data pre-processing through reward–punishment editing." Pattern Analysis and Applications 13, no. 4 (2010): 367–81. http://dx.doi.org/10.1007/s10044-010-0182-x.

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18

Seo, Hojin, Dohyun Kim, and Jai-Hyun Byun. "Data Pre-processing for Manufacturing Quality Improvement." Journal of the Korean Institute of Industrial Engineers 49, no. 3 (2023): 248–57. http://dx.doi.org/10.7232/jkiie.2023.49.3.248.

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19

Balamurugan, S., and A. B. Arockia Christopher. "Data Pre-Processing for Classification and Clustering." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 1–5. http://dx.doi.org/10.51983/ajcst-2012.1.1.1698.

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In real world datasets, lots of redundant and conflicting data exists. The performance of a classification algorithm in data mining is greatly affected by noisy information (i.e. redundant and conflicting data). These parameters not only increase the cost of mining process, but also degrade the detection performance of the classifiers. They have to be removed to increase the efficiency and accuracy of the classifiers. This process is called as the tuning of the dataset. The redundancy check will be performed on the original dataset and the resultant is to be preserved. This resultant dataset i

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20

Li, Yan, Decheng Wang, and Bo Yan. "Pre-processing study of multi-sensor data." International Journal of Computing and Information Technology 3, no. 1 (2023): 19. http://dx.doi.org/10.56028/ijcit.1.3.19.2023.

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For multi-sensor data with multi-source heterogeneity, data pre-processing is required for data fusion. This paper proposes a data pre-processing method, firstly, the image data is binarized using the designed image binarization algorithm and flame area extraction algorithm, and the flame area information in the fire image is extracted into one-dimensional data, then the five one-dimensional data of ambient temperature, temperature-sensitive cable, CO2, CO and flame area are noise reduced using the designed wavelet noise reduction algorithm, and finally the final pre-processed data is calculat

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21

Prakash, Dr A. Antony. "Pre-processing techniques for preparing clean and high-quality data for diabetes prediction." International Journal of Research Publication and Reviews 5, no. 2 (2024): 458–65. http://dx.doi.org/10.55248/gengpi.5.0224.0412.

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22

Baskar, S.S., L. Arockiam, and S. Charles. "A Systematic Approach on Data Pre-processing In Data Mining." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 11 (2013): 335–39. https://doi.org/10.5281/zenodo.14613431.

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Data pre-processing is an important and critical step in the data mining process and it has a huge impact on the success of a data mining Soil classification. Data pre-processing is a first step of the Knowledge discovery in databases (KDD) process that reduces the complexity of the data and offers better analysis and ANN training. Based on the collected data from the field as well soil testing laboratory, data analysis is performed more accurately and efficiently. Data pre-processing is challenging and tedious task as it involves extensive manual effort and time in developing the data operati

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23

Katser, Iurii D., Vyacheslav O. Kozitsin, Ivan V. Maksimov, Denis A. Larionov, and Konstantin I. Kotsoev. "Data pre-processing methods for NPP equipment diagnostics algorithms: an overview." Nuclear Energy and Technology 7, no. (2) (2021): 111–25. https://doi.org/10.3897/nucet.7.63675.

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The main tasks of diagnostics at nuclear power plants are detection, localization, diagnosis, and prognosis of the development of malfunctions. Analytical algorithms of varying degrees of complexity are used to solve these tasks. Many of these algorithms require pre-processed input data for high-quality and efficient operation. The pre-processing stage can help to reduce the volume of the analyzed data, generate additional informative diagnostic features, find complex dependencies and hidden patterns, discard uninformative source signals and remove noise. Finally, it can produce an improvement

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24

Robinson, Steve, Baskaran Narayanan, Nelson Toh, and Francisco Pereira. "Methods for pre-processing smartcard data to improve data quality." Transportation Research Part C: Emerging Technologies 49 (December 2014): 43–58. http://dx.doi.org/10.1016/j.trc.2014.10.006.

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25

Sapronova, Alla, and Thomas Marcher. "Improving Data Quality with Advanced Pre-Processing of MWD Data." Geotechnics 5, no. 2 (2025): 28. https://doi.org/10.3390/geotechnics5020028.

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In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially in complex geological settings. This study explores how the pre-processing of measurement while drilling (MWD) data impacts the accuracy of ML models. In this work, a correlational analysis of the MWD data is used as the main pre-processing procedure. For each drilling event

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26

Caten, Alexandre ten, Ricardo Simão Diniz Dalmolin, and Luis Fernando Chimelo Ruiz. "Digital soil mapping: strategy for data pre-processing." Revista Brasileira de Ciência do Solo 36, no. 4 (2012): 1083–92. http://dx.doi.org/10.1590/s0100-06832012000400003.

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The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled

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27

Mwai, Kennedy, Nelson Kibinge, James Tuju, et al. "protGear: A protein microarray data pre-processing suite." Computational and Structural Biotechnology Journal 19 (2021): 2518–25. http://dx.doi.org/10.1016/j.csbj.2021.04.044.

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28

R. Hasan, Ahmed, John E. Pattison, and Alex Hariz. "Pre-Processing of Affymetrix Gene Chip Microarray Data." Current Bioinformatics 5, no. 4 (2010): 270–79. http://dx.doi.org/10.2174/157489310794072526.

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29

Резова, Н. Л., Л. А. Казаковцев, Г. Ш. Шкаберина, and М. И. Цепкова. "DATA PRE-PROCESSING FOR COMPLEX SYSTEM BEHAVIOR ANALYSIS." СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, no. 2(88) (June 5, 2022): 40–45. http://dx.doi.org/10.36622/vstu.2022.88.2.008.

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Рассматривается развитие методов предварительной обработки данных с целью классификации больших объемов данных на примере задачи анализа поведения экосистем. Результаты вычислительного эксперимента показали целесообразность их использования. The article discusses the development of data preprocessing methods to classify large data amounts using the example of the problem of analyzing the behavior of ecosystems. The results of the computational experiment showed the feasibility of their use.

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Kutateladze, Rusudan, Ana Kobiashvili, and Nodar Darchiashvili. "Data Pre-Processing Procedures at the Call Cente." Works of Georgian Technical University, no. 3(521) (September 29, 2021): 40–46. http://dx.doi.org/10.36073/1512-0996-2021-3-40-46.

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Abstract. The procedures for pre-processing the data of the electronic call management system is implemented in the emergency call center. The purpose of the procedures is to process the raw data entered into the system to obtain statistical data. These procedures, in parallel with data entry, allow data to be converted and stored for later use. Following the introduction of this method, when receiving a statistical request, it is no longer necessary to reperform the procedure, because the data is already stored in the desired form. As a result, the duration of the work to be performed and the

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31

Czwick, Cordula, and Reiner Anderl. "Data Pre-Processing Method for Industrie 4.0 Applications." Procedia Computer Science 200 (2022): 327–36. http://dx.doi.org/10.1016/j.procs.2022.01.231.

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32

Abdul-Rahman, Shuzlina, Azuraliza Abu Bakar, and Zeti-Azura Mohamed-Hussein. "An intelligent data pre-processing of complex datasets." Intelligent Data Analysis 16, no. 2 (2012): 305–25. http://dx.doi.org/10.3233/ida-2012-0525.

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33

Barreto-Hernadez, Emiliano, Margarida Gama-Carvalho, and Lisete Sousa. "Pre-Processing Optimization of RNA Immunoprecipitation Microarray Data." Journal of Computational Biology 18, no. 10 (2011): 1319–28. http://dx.doi.org/10.1089/cmb.2010.0020.

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34

Xiang, C. Z., N. Herrmann, I. Deppner, et al. "The online data pre-processing for CBM-TOF." Journal of Instrumentation 8, no. 02 (2013): P02002. http://dx.doi.org/10.1088/1748-0221/8/02/p02002.

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35

Tatsumi, Daisuke, Yoshiki Tsunesada, and (the TAMA Collaboration). "Online calibration and pre-processing of TAMA data." Classical and Quantum Gravity 21, no. 5 (2004): S451—S456. http://dx.doi.org/10.1088/0264-9381/21/5/010.

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36

Zhao, Liangbin, Guoyou Shi, and Jiaxuan Yang. "Ship Trajectories Pre-processing Based on AIS Data." Journal of Navigation 71, no. 5 (2018): 1210–30. http://dx.doi.org/10.1017/s0373463318000188.

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Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands

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37

Berry, Anne, Alain Sigayret, and Christine Sinoquet. "Maximal sub-triangulation in pre-processing phylogenetic data." Soft Computing 10, no. 5 (2005): 461–68. http://dx.doi.org/10.1007/s00500-005-0507-7.

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38

Martin, Manon, Benoît Legat, Justine Leenders, et al. "PepsNMR for 1H NMR metabolomic data pre-processing." Analytica Chimica Acta 1019 (August 2018): 1–13. http://dx.doi.org/10.1016/j.aca.2018.02.067.

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39

Bilalli, Besim, Alberto Abelló, Tomàs Aluja-Banet, and Robert Wrembel. "PRESISTANT: Learning based assistant for data pre-processing." Data & Knowledge Engineering 123 (September 2019): 101727. http://dx.doi.org/10.1016/j.datak.2019.101727.

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40

Benhamed, Siham, and Safia Nait Bahloul. "Pre-processing of RDF data for METIS partitioning." International Journal of Metadata, Semantics and Ontologies 16, no. 2 (2023): 152–71. http://dx.doi.org/10.1504/ijmso.2023.135345.

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41

Tomar, Divya, and Sonali Agarwal. "A Survey on Pre-processing and Post-processing Techniques in Data Mining." International Journal of Database Theory and Application 7, no. 4 (2014): 99–128. http://dx.doi.org/10.14257/ijdta.2014.7.4.09.

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42

R., Prabha, and G. Kabadi Mohan. "A comprehensive insight towards pre-processing methodologies applied on GPS data." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2742–54. https://doi.org/10.11591/ijece.v10i3.pp2742-2754.

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as wel

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43

Li, Zheng-Heng, Jian-Chao Sun, Li-Ming Song, et al. "Observation data pre-processing and scientific data products generation of POLAR." Research in Astronomy and Astrophysics 19, no. 7 (2019): 091. http://dx.doi.org/10.1088/1674-4527/19/7/91.

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44

Duan, Yun Peng, Chun Xi Zhao, and Ying Shi. "Application of Data Pre-Processing Method in Web Mining." Applied Mechanics and Materials 687-691 (November 2014): 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1592.

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With the widely application of the WWW and the emergence of Web technology, make the research of data mining has entered a new stage. Web log mining is based on the idea of data mining to analyze the server log processing. Paper aimed at the early stage of the data mining is put forward based on log data preprocessing methods, the purpose is to divide server logs into multiple unique user access sequence at a time, and to give a good algorithm.

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Gemignani, Jessica, and Judit Gervain. "Comparing different pre-processing routines for infant fNIRS data." Developmental Cognitive Neuroscience 48 (April 2021): 100943. http://dx.doi.org/10.1016/j.dcn.2021.100943.

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46

Padmajavalli, R., and A. Jerline Amutha. "Pre-processing of Cardiology Data for Treadmill Test Prediction." International Journal of Web Technology 5, no. 1 (2016): 1–4. http://dx.doi.org/10.20894/ijwt.104.005.001.001.

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Paul, Ipsita. "Microarray Data Pre-processing for Feature Selection and Classification." Journal of Data Mining and Knowledge Engineering 6, no. 1 (2021): 36. http://dx.doi.org/10.47531/jodmke.6.1.2021.

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Dixit, Sameer, and Navjot Gwal. "An Implementation of Data Pre-Processing for Small Dataset." International Journal of Computer Applications 103, no. 6 (2014): 28–31. http://dx.doi.org/10.5120/18080-8707.

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Kobryń, Andrzej, and Joanna Prystrom. "A Data Pre-Processing Model for the Topsis Method." Folia Oeconomica Stetinensia 16, no. 2 (2016): 219–35. http://dx.doi.org/10.1515/foli-2016-0036.

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AbstractTOPSIS is one of the most popular methods of multi-criteria decision making (MCDM). Its fundamental role is the establishment of chosen alternatives ranking based on their distance from the ideal and negative-ideal solution. There are three primary versions of the TOPSIS method distinguished: classical, interval and fuzzy, where calculation algorithms are adjusted to the character of input rating decision-making alternatives (real numbers, interval data or fuzzy numbers). Various, specialist publications present descriptions on the use of particular versions of the TOPSIS method in the

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Hemmateenejad, Bahram, and Knut Baumann. "Data pre-processing for paper-based colorimetric sensor arrays." Chemometrics and Intelligent Laboratory Systems 254 (November 2024): 105237. http://dx.doi.org/10.1016/j.chemolab.2024.105237.

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