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: 7 September 2023

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Journal articles on the topic "Data Classification"

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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 (April 30, 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 (October 10, 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 (April 30, 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 (December 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 longitudinal quantitative data is presented which ultimately provides a description of both types of change over time. In so doing, this article argues for the increased use of ‘dirty data’ in longitudinal quantitative analysis, a step which allows for the exploration of both qualitative and quantitative changes to, and within, classification systems. This ultimately challenges existing assumptions relating to the quality and type of data used in quantitative research and how change in the social world is measured in general.
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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 (January 20, 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 hours of human experience) and compares favourably with other pretraining methods that require orders of magnitude more data. When paired with larger models and more diverse, task-aligned observational data, our methodology shows great promise, nearing human-level performance and data efficiency on Atari in the best-case scenario.
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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 (April 30, 2018): 745–49. http://dx.doi.org/10.31142/ijtsrd9566.

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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 (March 31, 2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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Bian, Jiang, Dayong Tian, Yuanyan Tang, and Dacheng Tao. "Trajectory Data Classification." ACM Transactions on Intelligent Systems and Technology 10, no. 4 (August 29, 2019): 1–34. http://dx.doi.org/10.1145/3330138.

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Suthaharan, Shan. "Big data classification." ACM SIGMETRICS Performance Evaluation Review 41, no. 4 (April 17, 2014): 70–73. http://dx.doi.org/10.1145/2627534.2627557.

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Anonymous. "Sonar data classification." Eos, Transactions American Geophysical Union 69, no. 38 (1988): 868. http://dx.doi.org/10.1029/88eo01128.

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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 factory and Teamcenter as the product data management software, are items that are affected data classification. In this report, all of these items will be explained separately. With the knowledge obtained about the above items, in the Volvo Cars Engine factory, the suitable hierarchical classification method is described. After defining the classification method, this method has been implemented in the software at the last part of the report to show that this method is executable.
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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 recordings using a high number of channels with the possibility to study the activity of large ensembles of neurons at a time. However, this fascinating opportunity introduces at the same time a challenge for the efficient and fast analysis of this data. In this research work, we address this problem by developing a new implementation for unsupervised spike sorting that improves the performance of a widely-used spike sorting algorithm, increasing the number of automatically identified neurons. Moreover, we developed a new testing platform which generates simulations of extracellular recordings including challenging conditions such as realistic noise, multi-unit activity -spikes of distant neurons impossible to be identified as single units- or the presence of neurons with low firing rates. In summary, the results presented here provide contributions to the development of automated and efficient quantitative frameworks for the analysis of multiple-channel recordings that help us to understand single-neuron population codes.
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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 and its termination after finite time is proved for empirical distributions. The construction of a particular classification method is based on a statistical information measure specified by a convex function. Modifying this convex function gives room for suggesting a large variety of new classification procedures, e.g. of robust quantifiers. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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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 time, and is considered functional data.With functional data analysis (FDA), a process can be analysed as a continuous function of time,instead of being reduced to finite data points. FDA includes many useful tools, but also somechallenges. A functional observation can for example be differentiated, a handy tool not found inthe multivariate tool-box. The speed, and acceleration, can then be calculated from the obtaineddata. How to define "similarity" is, on the other hand, not as obvious as with points. In this work,an FDA-approach is taken on classifying knee kinematic data, from a long-term follow-up studyon knee ligament injuries.This work studies kernel functional classifiers, and k-nearest neighbours models, and performssignificance tests on the model accuracy, using re-sampling methods. Additionally, depending onhow similarity is defined, the models can distinguish different features of the data. Attempts atutilising more information through incorporation of ensemble-methods, does not exceed the singlemodels it is created from. Further, it is shown that classification on optimised sub-domains, canbe superior to classifiers using the full domain, in terms of predictive power.
Främre korsbandsskador är vanliga och välkända skador, speciellt bland idrottsutövare. Skadornakräver ofta operationer och långa rehabiliteringsprogram, och kan leda till funktionell nedsättningoch återskador (Marshall et al., 1977). Målet med det här arbetet är att utforska möjligheten attklassificera knän utifrån funktionalitet, där utfallet är känt. Detta genom att använda olika typerav modeller, och genom att testa olika indelningar av grupper. Datat som används är insamlatunder ett prestandatest, där personer hoppat på ett ben med rörelsesensorer på kroppen. Deninsamlade datan representerar position över tid, och betraktas som funktionell data.Med funktionell dataanalys (FDA) kan en process analyseras som en kontinuerlig funktion av tid,istället för att reduceras till ett ändligt antal datapunkter. FDA innehåller många användbaraverktyg, men även utmaningar. En funktionell observation kan till exempel deriveras, ett händigtverktyg som inte återfinns i den multivariata verktygslådan. Hastigheten och accelerationen kandå beräknas utifrån den insamlade datan. Hur "likhet" är definierat, å andra sidan, är inte likauppenbart som med punkt-data. I det här arbetet används FDA för att klassificera knärörelsedatafrån en långtidsuppföljningsstudie av främre korsbandsskador.I detta arbete studeras både funktionella kärnklassificerare och k-närmsta grannar-metoder, och ut-för signifikanstest av modellträffsäkerheten genom omprovtagning. Vidare kan modellerna urskiljaolika egenskaper i datat, beroende på hur närhet definieras. Ensemblemetoder används i ett försökatt nyttja mer av informationen, men lyckas inte överträffa någon av de enskilda modellerna somutgör ensemblen. Vidare så visas också att klassificering på optimerade deldefinitionsmängder kange en högre förklaringskraft än klassificerare som använder hela definitionsmängden.
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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
Ph.D.
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 faced with the small-sample high-dimensional microarray data, most machine learning algorithm would produce an overly complicated model that performs well on training data but poorly on new data. To reduce the risk of over-fitting, feature selection becomes an essential technique in microarray classification. However, standard feature selection algorithms are bound to underperform when the size of the microarray data is particularly small. The best remedy is to borrow strength from external microarray datasets. In this dissertation, I will present two new multi-task feature filter methods which can improve the classification performance by utilizing the external microarray data. The first method is to aggregate the feature selection results from multiple microarray classification tasks. The resulting multi-task feature selection can be shown to improve quality of the selected features and lead to higher classification accuracy. The second method jointly selects a small gene set with maximal discriminative power and minimal redundancy across multiple classification tasks by solving an objective function with integer constraints. In protein function prediction problem, gene functions are predicted from a predefined set of possible functions (e.g., the functions defined in the Gene Ontology). Gene function prediction is a complex classification problem characterized by the following aspects: (1) a single gene may have multiple functions; (2) the functions are organized in hierarchy; (3) unbalanced training data for each function (much less positive than negative examples); (4) missing class labels; (5) availability of multiple biological data sources, such as microarray data, genome sequence and protein-protein interactions. As participants in the 2011 Critical Assessment of Function Annotation (CAFA) challenge, our team achieved the highest AUC accuracy among 45 groups. In the competition, we gained by focusing on the 5-th aspect of the problem. Thus, in this dissertation, I will discuss several schemes to integrate the prediction scores from multiple data sources and show their results. Interestingly, the experimental results show that a simple averaging integration method is competitive with other state-of-the-art data integration methods. Original spatial scan algorithm is used for detection of spatial overdensities: discovery of spatial subregions with significantly higher scores according to some density measure. This algorithm is widely used in identifying cluster of disease cases (e.g., identifying environmental risk factors for child leukemia). However, the original spatial scan algorithm only works on static spatial data. In this dissertation, I will propose one possible solution for spatial scan on movement data.
Temple University--Theses
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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 classification process. This thesis is mainly focused on building a training set from some already given data, and specially on condensing it; however different classification techniques are also compared.
The aim of any condensing technique is to obtain a reduced training set in order to spend as few time as possible in classification. All that without a significant loss in classification accuracy. Some
new approaches to training set size reduction based on prototypes are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. That includes those approaches that select among the already existing examples (selective condensing algorithms), and those which generate new representatives (adaptive condensing algorithms).
Those new reduction techniques are experimentally compared to some traditional ones, for data represented in feature spaces. In order to test them, the classical 1-NN rule is here applied. However, other classifiers (fast classifiers) have been considered here, as linear and quadratic ones constructed in dissimilarity spaces based on prototypes, in order to realize how editing and condensing concepts work for this different family of classifiers.
Although the goal of the algorithms proposed in this thesis is to obtain a strongly reduced set of representatives, the performance is empirically evaluated over eleven real data sets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques. Therefore, the ultimate aim is not only to find a strongly reduced set, but also a balanced one.
Several ways to solve the same problem could be found. So, in the case of using a rule based on distance as a classifier, not only the option of reducing the training set can be afford. A different family of approaches consists of applying several searching methods. Therefore, results obtained by the use of the algorithms here presented are compared in terms of classification accuracy and time, to several efficient search techniques.
Finally, the main contributions of this PhD report could be briefly summarised in four principal points. Firstly, two selective algorithms based on the idea of surrounding neighbourhood. They obtain better results than other algorithms presented here, as well as better than other traditional schemes. Secondly, a generative approach based on mixtures of Gaussians. It presents better results in classification accuracy and size reduction than traditional adaptive algorithms, and similar to those of the LVQ. Thirdly, it is shown that classification rules other than the 1-NN can be used, even leading to better results. And finally, it is deduced from the experiments carried on, that with some databases (as the ones used here) the approaches here presented execute the classification processes in less time that the efficient search techniques.
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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 range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.
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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 method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.
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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. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72087-1.

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Data analysis, data modeling, and classification. New York: McGraw-Hill, 1992.

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Jajuga, Krzysztof, Krzysztof Najman, and Marek Walesiak, eds. Data Analysis and Classification. Cham: 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. Berlin, Heidelberg: 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. Cham: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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

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Book chapters on the topic "Data Classification"

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Runkler, Thomas A. "Classification." In Data Analytics, 85–101. Wiesbaden: 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, 91–109. Wiesbaden: 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, 95–115. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29779-4_8.

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Christen, Peter. "Classification." In Data Matching, 129–62. Berlin, Heidelberg: 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, 285–344. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_10.

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Bergel, Alexandre. "Data Classification." In Agile Artificial Intelligence in Pharo, 89–116. Berkeley, CA: 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, 113–41. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2250-8_8.

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Slocum, Terry A., Robert B. McMaster, Fritz C. Kessler, and Hugh H. Howard. "Data Classification." In Thematic Cartography and Geovisualization, 83–98. 4th ed. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003150527-6.

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Toelle, Erica. "Data Classification." In Microsoft 365 Compliance, 61–99. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-5778-4_3.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "Classification." In Studies in Big Data, 287–308. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_13.

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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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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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Ferrara, Alfio, Lorenzo Genta, and Stefano Montanelli. "Linked data classification." In the Joint EDBT/ICDT 2013 Workshops. New York, New York, USA: 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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Heasley, J. N., and Coryn A. L. Bailer-Jones. "The Pan-STARRS Data Processing and Science Analysis Software Systems." 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/10.1063/1.3059075.

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He, Jing Selena, Meng Han, Lei Yu, and Chao Mei. "Lung Pattern Classification Via DCNN." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378090.

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Zhang, Jason, Krerkkiat Chusap, Wei Zhang, and Chang Liu. "Improving Paper Classification Using Forecasting." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020764.

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Shinde, Neeta, Chandana S, Shashank Anand Patil, K. Siri Chandana, Neha Tarannum Pendari, P. G. Sunitha Hiremath, and Shankar Gangisetty. "Stacked LSTM Based Wafer Classification." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671835.

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Reports on the topic "Data Classification"

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Newhouse, Bill. Implementing Data Classification Practices. Gaithersburg, MD: 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. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1008925.

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Gorsich, David J., Robert E. Karlsen, and Grant R. Gerhart. Classification Consequences of Preprocessing Radar Data. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada457937.

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Jimenez, Luis O., Miguel Velez, and Shawn Hunt. Unsupervised Classification System for Hyperspectral Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 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), February 1998. http://dx.doi.org/10.2172/8300.

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Kirby, Michael, and Chris Peterson. Classification of Data Bundles via Parameter Spaces. Fort Belvoir, VA: Defense Technical Information Center, December 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), September 2005. http://dx.doi.org/10.2172/925358.

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Hawkins, Rupert S., K. F. Heideman, and Ira G. Smotroff. Cloud Data Set for Neural Network Classification Studies. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada256181.

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Maule, R. W., Gordon Schacher, Shelley Gallup, Charles Marashian, and Bryan McClain. Ethnographic Qualitative Knowledge Management System Data Classification Schema. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada384029.

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Staenz, K., J. W. Schwarz, L. Vernaccini, F. Vachon, and C. Nadeau. Classification of Hyperspectral Agricultural Data with Spectral Matching Techniques. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219608.

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