Relevant bibliographies by topics / Data Classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data Classification"

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Sutanapong., Chanoknath, and P. Louangrath. "Data Classification and Distribution." Inter. J. Res. Methodol. Soc. Sci 1, no. 2 (2015): 36–47. https://doi.org/10.5281/zenodo.1320784.

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The objective of this paper is to explain the four main types of data. The classification of data by type is important for statistical analysis. In particular, data classification is useful for quantitative research in social science. Data are defined as a quantitative measurement of qualitative fact. Data are classified into three types: quantitative, ordinal and nominal. Quantitative data are those that may be subject to mathematical operations: addition, subtraction, multiplication and division. Ordinal data are those that rank the values in a data set in an ascending order (from low to hig
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Smita, Nirkhi, and Patil Shashikant. "Comprehensive Assessment of Imbalanced Data Classification." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1426–31. https://doi.org/10.35940/ijeat.D7349.049420.

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This is an attempt to address the various challenges opportunities and scope for formulating and designing new procedure in imbalanced classification problem which poses a challenge to a predictive modelling as many of AI ML n DL algorithms which are extensively used for classification are always designed from the perspective of with majority of focus on assuming equal number of examples for a class. It leads to poor efficiency and performance especially in minority class. As Minority class is always very crucial and sensitive to classification errors and also its utmost important in imbalance
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Geethika, Paruchuri, and Voleti Prasanthi. "Booster in High Dimensional Data Classification." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1186–90. http://dx.doi.org/10.31142/ijtsrd11368.

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Alhaisoni, Majed Mohaia, Rabie A. Ramadan, and Ahmed Y. Khedr. "SCF: Smart Big Data Classification Framework." Indian Journal of Science and Technology 12, no. 37 (2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i37/148647.

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S, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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Uprichard, Emma. "Dirty Data: Longitudinal Classification Systems." Sociological Review 59, no. 2_suppl (2011): 93–112. http://dx.doi.org/10.1111/j.1467-954x.2012.02058.x.

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Typically in longitudinal quantitative research, classifications are tracked over time. However, most classifications change in absolute terms in that some die whilst others are created, and in their meaning. There is a need, therefore, to re-think how longitudinal quantitative research might explore both the qualitative changes to classification systems as well as the quantitative changes within each classification. By drawing on the changing classifications of local food retail outlets in the city of York (UK) since the 1950s as an illustrative example, an alternative way of graphing longitu
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Anam, Mamoona, Dr Kantilal P. Rane, Ali Alenezi, Ruby Mishra, Dr Swaminathan Ramamurthy, and Ferdin Joe John Joseph. "Content Classification Tasks with Data Preprocessing Manifestations." Webology 19, no. 1 (2022): 1413–30. http://dx.doi.org/10.14704/web/v19i1/web19094.

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Deep reinforcement learning has a major hurdle in terms of data efficiency. We solve this challenge by pretraining an encoder with unlabeled input, which is subsequently finetuned on a tiny quantity of task-specific input. We use a mixture of latent dynamics modelling and unsupervised goal-conditioned RL to encourage learning representations that capture various elements of the underlying MDP. Our approach significantly outperforms previous work combining offline representation pretraining with task-specific finetuning when limited to 100k steps of interaction on Atari games (equivalent to two
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Rani, A. Nithya, and Dr Antony Selvdoss Davamani. "Classification on Missing Data for Multiple Imputations." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 745–49. http://dx.doi.org/10.31142/ijtsrd9566.

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Gniazdowski, Zenon, and Michał Grabowski. "Numerical Coding of Nominal Data." Zeszyty Naukowe WWSI 9, no. 12 (2015): 53——61. https://doi.org/10.26348/znwwsi.12.53.

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In this paper, a novel approach for coding nominal data is proposed. For the given nominal data, a rank in a form of complex number is assigned. The proposed method does not lose any information about the attribute and brings other properties previously unknown. The approach based on these knew properties can been used for classification. The analyzed example shows that classification with the use of coded nominal data or both numerical as well as coded nominal data is more effective than the classification, which uses only numerical data.
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N.J., Anjala. "Algorithmic Assessment of Text based Data Classification in Big Data Sets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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Dissertations / Theses on the topic "Data Classification"

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Morshedzadeh, Iman. "Data Classification in Product Data Management." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14651.

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This report is about the product data classification methodology that is useable for the Volvo Cars Engine (VCE) factory's production data, and can be implemented in the Teamcenter software. There are many data generated during the life cycle of each product, and companies try to manage these data with some product data management software. Data classification is a part of data management for most effective and efficient use of data. With surveys that were done in this project, items affecting the data classification have been found. Data, attributes, classification method, Volvo Cars Engine f
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Currie, Sheila. "Data classification for choropleth mapping." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5725.

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Gómez, Juan Martínez. "Automatic classification of neural data." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9696.

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In this thesis we present a new solution for an automatic classification of the single-neuron activity. The study of the computational role of individual neurons underlying different cognitive process is a gold standard in Neuroscience. This type of analysis is done first, by recording the extracellular spikes of the neurons near the tip of a microelectrode and second, by isolating the spikes of the recorded cells based on the similarity of their shapes using a method called spike sorting. In recent years, important advances in microelectrode technology allow us now to perform massive parallel
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Pötzelberger, Klaus, and Helmut Strasser. "Data Compression by Unsupervised Classification." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/974/1/document.pdf.

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This paper deals with a general class of classification methods which are related both to vector quantization in the sense of Pollard, [12], as well as to competitive learning in the sense of Kohonen, [10]. The basic duality of minimum variance partitioning and vector quantization known from statistical cluster analysis is shown to be true for this whole class of classification problems. The paper contains theoretical results like existence of optima, consistency of approximate optima and characterization of local optima as fixpoints of a fix point algorithm. A fix point algorithm is proposed
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Soukhoroukova, Nadejda. "Data classification through nonsmooth optimization." Thesis, University of Ballarat [Mt. Helen, Vic.] :, 2003. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/42220.

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Kröger, Viktor. "Classification in Functional Data Analysis : Applications on Motion Data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184963.

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Anterior cruciate knee ligament injuries are common and well known, especially amongst athletes.These injuries often require surgeries and long rehabilitation programs, and can lead to functionloss and re-injuries (Marshall et al., 1977). This work aims to explore the possibility of applyingsupervised classification on knee functionality, using different types of models, and testing differentdivisions of classes. The data used is gathered through a performance test, where individualsperform one-leg hops with motion sensors attached to their bodies. The obtained data representsthe position over
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Lan, Liang. "Data Mining Algorithms for Classification of Complex Biomedical Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214773.

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Computer and Information Science<br>Ph.D.<br>In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray classification, samples belong to several predefined categories (e.g., cancer vs. control tissues) and the goal is to build a predictor that classifies a new tissue sample based on its microarray measurements. When
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Lozano, Albalate Maria Teresa. "Data Reduction Techniques in Classification Processes." Doctoral thesis, Universitat Jaume I, 2007. http://hdl.handle.net/10803/10479.

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The learning process consists of different steps: building a Training Set (TS), training the system, testing its behaviour and finally classifying unknown objects. When using a distance based rule as a classifier, i.e. 1-Nearest Neighbour (1-NN), the first step (building a training set) includes editing and condensing data. The main reason for that is that the rules based on distance need many time to classify each unlabelled sample, x, as each distance from x to each point in the training set should be calculated. So, the more reduced the training set, the shorter the time needed for each new
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Aygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.

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In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target ra
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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used metho
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Books on the topic "Data Classification"

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Balderjahn, Ingo, Rudolf Mathar, and Martin Schader, eds. Classification, Data Analysis, and Data Highways. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72087-1.

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Jajuga, Krzysztof, Krzysztof Najman, and Marek Walesiak, eds. Data Analysis and Classification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75190-6.

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Batagelj, Vladimir, Hans-Hermann Bock, Anuška Ferligoj, and Aleš Žiberna, eds. Data Science and Classification. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34416-0.

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Jajuga, Krzysztof, Jacek Batóg, and Marek Walesiak, eds. Classification and Data Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52348-0.

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Palumbo, Francesco, Carlo Natale Lauro, and Michael J. Greenacre, eds. Data Analysis and Classification. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-03739-9.

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Vichi, Maurizio, and Otto Opitz, eds. Classification and Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2.

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Giusti, Antonio, Gunter Ritter, and Maurizio Vichi, eds. Classification and Data Mining. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28894-4.

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Giusti, Antonio. Classification and Data Mining. Springer Berlin Heidelberg, 2013.

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Jajuga, Krzysztof, Grażyna Dehnel, and Marek Walesiak, eds. Modern Classification and Data Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10190-8.

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Jajuga, Krzysztof, Andrzej Sokołowski, and Hans-Hermann Bock, eds. Classification, Clustering, and Data Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8.

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Book chapters on the topic "Data Classification"

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Runkler, Thomas A. "Classification." In Data Analytics. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-2589-6_8.

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Runkler, Thomas A. "Classification." In Data Analytics. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14075-5_8.

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Runkler, Thomas A. "Classification." In Data Analytics. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29779-4_8.

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Runkler, Thomas A. "Classification." In Data Analytics. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45951-2_8.

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Christen, Peter. "Classification." In Data Matching. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31164-2_6.

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Aggarwal, Charu C. "Data Classification." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_10.

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. "Unsupervised Classification." In Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3_5.

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. "Supervised Classification." In Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3_6.

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Bergel, Alexandre. "Data Classification." In Agile Artificial Intelligence in Pharo. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5384-7_5.

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Paluszek, Michael, and Stephanie Thomas. "Data Classification." In MATLAB Machine Learning. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2250-8_8.

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Conference papers on the topic "Data Classification"

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"Content-Adaptive Data Fusion." In The Second International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001222100230032.

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MILVANG, O., R. BANG HUSEBY, K. WEISTEEN, and A. SOLBERG. "FEATURE EXTRACTION FROM BACKSCATTER SONAR DATA." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20439.

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Simas, Tiago, Gabriel Silva, Bruno Miranda, Andre Moitinho, Rita Ribeiro, and Coryn A. L. Bailer-Jones. "Knowledge Discovery in Large Data Sets." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059044.

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Borne, K., J. Becla, I. Davidson, A. Szalay, J. A. Tyson, and Coryn A. L. Bailer-Jones. "The LSST Data Mining Research Agenda." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059074.

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Bonner, Stephen, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, and Andrew Stephen McGough. "Deep topology classification: A new approach for massive graph classification." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840988.

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BAXTER, P., and A. SETTERY. "DIGITAL ACQUISITION OF HIGH RESOLUTION ACOUSTIC SURVEY DATA." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20440.

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MAKRIS, NC, and JM BERKSON. "INVERSION AND MAPPING OF TOWED-ARRAY REVERBERATION DATA." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20436.

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BANG HUSEBY, R., O. MILVANG, A. SOLBERG, and K. WEISTEEN. "SEABED CLASSIFICATION FROM BACKSCATTER SONAR DATA USING STATISTICAL METHODS." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20446.

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Ferrara, Alfio, Lorenzo Genta, and Stefano Montanelli. "Linked data classification." In the Joint EDBT/ICDT 2013 Workshops. ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457330.

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Annapoorna, P. V. Srilakshmi, and T. T. Mirnalinee. "Streaming data classification." In 2016 Fifth International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2016. http://dx.doi.org/10.1109/icrtit.2016.7569525.

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Reports on the topic "Data Classification"

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Newhouse, Bill. Implementing Data Classification Practices. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.sp.1800-39.ipd.

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Fiebiger, Frank. Map Classification In Image Data. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1008925.

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Melad, Kris Ann. Harmonizing Philippine Census Data across Decades (1970–2020). Philippine Institute for Development Studies, 2024. https://doi.org/10.62986/dp2024.44.

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This research harmonizes Philippine Census of Population and Housing (CPH) data from 1970 to 2020 to address data consistency challenges across five decades. The study systematically reconciles evolving variable definitions, classification systems, and measurement scales to create a unified longitudinal dataset. Key harmonization challenges include accommodating changes in the education system, such as the K-12 reforms, tracking modifications to administrative boundaries over the years, managing the expanding data scope across census years, and addressing historical data preservation issues, p
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Gorsich, David J., Robert E. Karlsen, and Grant R. Gerhart. Classification Consequences of Preprocessing Radar Data. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada457937.

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Souppaya, Murugiah. Data Classification Concepts and Considerations for Improving Data Protection. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8496.ipd.

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Newhouse, Bill. Data Classification Concepts and Considerations for Improving Data Protection. National Institute of Standards and Technology, 2025. https://doi.org/10.6028/nist.cswp.43.

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Jimenez, Luis O., Miguel Velez, and Shawn Hunt. Unsupervised Classification System for Hyperspectral Data Analysis. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada398803.

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Lades, M. Motion description for data compression and classification. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/8300.

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Kirby, Michael, and Chris Peterson. Classification of Data Bundles via Parameter Spaces. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada563706.

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Vilim, R. B., E. E. Feldman, W. D. Pointer, and T. Y. C. Wei. Initial VHTR accident scenario classification: models and data. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/925358.

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