Data Preprocessing: A Step-by-Step Guide for Clean and Usable Data
Main Article Content
Abstract
Data preprocessing is an crucial phase within the records evaluation pipeline, pivotal in ensuring that uncooked statistics may be efficiently utilized to extract meaningful insights. This evaluates paper offers a complete manual to the intricate process of statistics preprocessing, providing a step-by using-step assessment of essential strategies and best practices for refining uncooked records into smooth and usable datasets. The paper encompasses diverse facts preprocessing obligations, including records cleaning, lacking price imputation, function engineering, and outlier detection, underscoring their important position in improving statistics great. Drawing from the modern studies and sensible tips, this review equips facts analysts and scientists
with the expertise and gear needed to bolster information reliability and relevance in a mess of programs.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Gantz, J.; Reinsel, D. The Digital Universe in 2020: Big Data, Bigger Digital Shadows, And Biggest Growth
in the Far East (accessed on 20 April 2018).
Hu, H.; Wen, Y.; Chua, T.S.; Li, X. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial.
IEEE Access 2014, 2, 652–687
Rajaraman, A.; Ullman, J.D. Mining of Massive Datasets; Cambridge University Press: New York, NY, USA;
Cambridge, UK, 2011.
Pacheco, F.; Rangel, C.; Aguilar, J.; Cerrada, M.; Altamiranda, J. Methodological framework for data
processing based on the Data Science paradigm. In Proceedings of the 2014 XL Latin American Computing
Conference (CLEI), Montevideo, Uruguay, 15–19 September 2014; pp. 1–12.
Sebastian-Coleman, L. Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment
Framework; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 2012.
Eyob, E. Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and
Solutions: Interdisciplinary Frameworks and Solutions; Information Science Reference: Hershey, PA, USA, 2009.
Piateski, G.; Frawley, W. Knowledge Discovery in Databases; MIT Press: Cambridge, MA, USA, 1991.
Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA Data Mining Software:
An Update. SIGKDD Explor. Newsl. 2009, 11, 10–18.
Mierswa, I.; Wurst, M.; Klinkenberg, R.; Scholz, M.; Euler, T. YALE: Rapid Prototyping for Complex Data
Mining Tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining, Philadelphia, PA, USA, 20–23 August 2006; ACM: New York, NY, USA, 2006; pp. 935–940.
Berthold, M.; Cebron, N.; Dill, F.; Gabriel, T.; Kötter, T.; Meinl, T.; Ohl, P.; Thiel, K.; Wiswedel, B.
KNIME—The Konstanz information miner: Version 2.0 and Beyond. ACM SIGKDD Explor. Newsl. 2009, 11,
–31
MATHWORKS. Matlab; The MathWorks Inc.: Natick, MA, USA, 2004
Ihaka, R.; Gentleman, R. R: A language for data analysis and graphics. J. Comput. Graph. Stat. 1996, 5, 299–
14.
Eaton, J.W. GNU Octave Manual; Network Theory Limited: Eastbourne, UK, 2002.
Corrales, D.C.; Ledezma, A.; Corrales, J.C. A Conceptual Framework for Data Quality in Knowledge
Discovery Tasks (FDQ-KDT): A Proposal. J. Comput. 2015, 10, 396–405.
Caballero, I.; Verbo, E.; Calero, C.; Piattini, M. A Data Quality Measurement Information Model Based on
ISO/IEC 15939; ICIQ: Cambridge, MA, USA, 2007; pp. 393–408.
Ballou, D.P.; Pazer, H.L. Modeling Data and Process Quality in Multi-Input, Multi-Output Information
Systems. Manag. Sci. 1985, 31, 150–162
Berti-Équille, L. Measuring and Modelling Data Quality for Quality-Awareness in Data Mining. In Quality
Measures in Data Mining; Guillet, F.J., Hamilton, H.J., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; pp.
–126.
Kerr, K.; Norris, T. The Development of a Healthcare Data Quality Framework and Strategy. In Proceedings
of the Ninth International Conference on Information Quality (ICIQ-04), Cambridge, MA, USA, 5–7 November
; pp. 218–233.
Wang, R.Y.; Strong, D.M. Beyond accuracy: What data quality means to data consumers. J. Manag. Inf. Syst.
, 12, 5–33.
R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid
Connected Solar PV System", 2018 3rd International Conference and Workshops on Recent Advances and
Innovations in Engineering (ICRAIE), pp. 1-4, 2018.