Comparing Discrete and Continuous Data: Concepts, Differences, and Applications.

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Header Abstract Content Author and article information References Comments

Record: found
Abstract: found
Article: found

Is Open Access

Comparing Discrete and Continuous Data: Concepts, Differences, and Applications.

Preprint

In review

research-article

Author(s): Akpusugh Verse Ephraim Odvese ¹ ^,

Other contributor(s): Dekpoghol Tertsea ² (Research-assistant)

Publication date (Electronic preprint): 1 November 2024

Journal: ScienceOpen Preprints

Publisher: ScienceOpen

Keywords: Discrete data, Continuous data, Bar charts, Pie charts, Data Visualization, Mean, Median , Standard Deviation , Data analysis, Whole numbers

Bookmark

Abstract

Data is usually divided into two types: discrete and continuous, both of which play important roles in subjects such as statistics, mathematics, computer science, and data analysis. Understanding the distinctions between discrete and continuous data is critical for selecting proper data analysis techniques, statistical tests, and graphical representations. Discrete data shows countable values or separate categories that are often derived from a finite set, such as integers or category labels. This data type is distinguished by solitary points and is frequently found in survey replies, customer counts, or categorical variables (for example, colors types). Discrete data is commonly represented using bar charts or pie charts, and analysis are frequently performed using chi-square tests or other non-parametric approaches that account for discrete distributions. Continuous data, on the other hand, includes an infinite number of possible values within a specific range, and is frequently used to represent measurable quantities such as time, temperature, height, or distance. Continuous data points are on a continuum and can take any value within a certain interval, tailoring them for expressing phenomena that fluctuate smoothly across time or space. Continuous data, due to its mathematical qualities, can be evaluated with a variety of parametric tests, including t-tests and ANOVA, and is frequently represented using histograms or line charts to show data density or trends over time. Data processing, analysis techniques, and computational complexity are all radically altered by the distinction between discrete and continuous data. Because of its finite nature, discrete data frequently calls for simpler computing models and storage, whereas continuous data usually necessitates more intricate data handling and modeling procedures because of its infinite granularity. This paper explores the differences between discrete and continuous data in data science and machine learning, giving prominence to selecting appropriate algorithms for each type. It emphasizes the advantages and disadvantages of each type in various applications and provides recommendations for data handling and analysis techniques.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 1 November 2024

Affiliations

[1 ] Department of Mathematics, Federal University of Technology Minna, Nigeria.;

[2 ] Department of Chemistry, University of Abuja, FCT Nigeria.;

Author notes

[* ]Email: akpusughephraim@ 123456gmail.com .

Author information

Akpusugh Verse Ephraim Odvese https://orcid.org/0009-0002-9520-4868

Dekpoghol Tertsea https://orcid.org/0009-0008-6399-2314

Article

DOI: 10.14293/PR2199.001249.v1

SO-VID: 52b1eb8e-3360-455d-a2fb-3a5d4745e7a0

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 1 November 2024

References

Alstott Jeff, Bullmore Ed, Plenz Dietmar. powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions. PLoS ONE. Vol. 9(1)2014. Public Library of Science (PLoS). [Cross Ref]
Capó Marco, Pérez Aritz, Lozano Jose A.. An efficient K-means clustering algorithm for tall data. Data Mining and Knowledge Discovery. Vol. 34(3):776–811. 2020. Springer Science and Business Media LLC. [Cross Ref]
Cartuyvels Ruben, Spinks Graham, Moens Marie-Francine. Discrete and continuous representations and processing in deep learning: Looking forward. AI Open. Vol. 2:143–159. 2021. Elsevier BV. [Cross Ref]
Liu Manxia, Stella Fabio, Hommersom Arjen, Lucas Peter J.F., Boer Lonneke, Bischoff Erik. A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. Artificial Intelligence in Medicine. Vol. 95:104–117. 2019. Elsevier BV. [Cross Ref]
Delignette-Muller Marie Laure, Dutang Christophe. fitdistrplus: AnRPackage for Fitting Distributions. Journal of Statistical Software. Vol. 64(4)2015. Foundation for Open Access Statistic. [Cross Ref]
Kaur Harpreet. Analysis of Various Optimization Techniques in Machine Learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT). Vol. 12(2):472–482. 2021. Ninety Nine Publication. [Cross Ref]
Khetwal Anuradha, Einstein Herbert H., Rostami Jamal. Combining the CSM2020 discrete event simulation model with Decision Aid in Tunneling (DAT) to develop a robust approach for the estimation of completion time for TBM tunnels. Tunnelling and Underground Space Technology. Vol. 138:2023. Elsevier BV. [Cross Ref]
Leibovich Tali, Henik Avishai. Comparing Performance in Discrete and Continuous Comparison Tasks. Quarterly Journal of Experimental Psychology. Vol. 67(5):899–917. 2014. SAGE Publications. [Cross Ref]
Liu Manxia, Stella Fabio, Hommersom Arjen, Lucas Peter J.F., Boer Lonneke, Bischoff Erik. A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. Artificial Intelligence in Medicine. Vol. 95:104–117. 2019. Elsevier BV. [Cross Ref]
Schöllhorn W.I, Nigg B.M, Stefanyshyn D.J, Liu W. Identification of individual walking patterns using time discrete and time continuous data sets. Gait & Posture. Vol. 15(2):180–186. 2002. Elsevier BV. [Cross Ref]
Shen Jun, Lam James. <inline-formula> <tex-math notation="TeX">$\ell_{\infty}/L_{\infty} $</tex-math></inline-formula>-Gain Analysis for Positive Linear Systems With Unbounded Time-Varying Delays. IEEE Transactions on Automatic Control. Vol. 60(3):857–862. 2015. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

Comments

Comment on this article

[1] Alstott Jeff, Bullmore Ed, Plenz Dietmar. powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions. PLoS ONE. Vol. 9(1)2014. Public Library of Science (PLoS). [Cross Ref]

[2] Capó Marco, Pérez Aritz, Lozano Jose A.. An efficient K-means clustering algorithm for tall data. Data Mining and Knowledge Discovery. Vol. 34(3):776–811. 2020. Springer Science and Business Media LLC. [Cross Ref]

[3] Cartuyvels Ruben, Spinks Graham, Moens Marie-Francine. Discrete and continuous representations and processing in deep learning: Looking forward. AI Open. Vol. 2:143–159. 2021. Elsevier BV. [Cross Ref]

[4] Liu Manxia, Stella Fabio, Hommersom Arjen, Lucas Peter J.F., Boer Lonneke, Bischoff Erik. A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. Artificial Intelligence in Medicine. Vol. 95:104–117. 2019. Elsevier BV. [Cross Ref]

[5] Delignette-Muller Marie Laure, Dutang Christophe. <b>fitdistrplus</b>: An<i>R</i>Package for Fitting Distributions. Journal of Statistical Software. Vol. 64(4)2015. Foundation for Open Access Statistic. [Cross Ref]

[6] Kaur Harpreet. Analysis of Various Optimization Techniques in Machine Learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT). Vol. 12(2):472–482. 2021. Ninety Nine Publication. [Cross Ref]

[7] Khetwal Anuradha, Einstein Herbert H., Rostami Jamal. Combining the CSM2020 discrete event simulation model with Decision Aid in Tunneling (DAT) to develop a robust approach for the estimation of completion time for TBM tunnels. Tunnelling and Underground Space Technology. Vol. 138:2023. Elsevier BV. [Cross Ref]

[8] Leibovich Tali, Henik Avishai. Comparing Performance in Discrete and Continuous Comparison Tasks. Quarterly Journal of Experimental Psychology. Vol. 67(5):899–917. 2014. SAGE Publications. [Cross Ref]

[9] Liu Manxia, Stella Fabio, Hommersom Arjen, Lucas Peter J.F., Boer Lonneke, Bischoff Erik. A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. Artificial Intelligence in Medicine. Vol. 95:104–117. 2019. Elsevier BV. [Cross Ref]

[10] Schöllhorn W.I, Nigg B.M, Stefanyshyn D.J, Liu W. Identification of individual walking patterns using time discrete and time continuous data sets. Gait & Posture. Vol. 15(2):180–186. 2002. Elsevier BV. [Cross Ref]

[11] Shen Jun, Lam James. <inline-formula> <tex-math notation="TeX">$\ell_{\infty}/L_{\infty} $</tex-math></inline-formula>-Gain Analysis for Positive Linear Systems With Unbounded Time-Varying Delays. IEEE Transactions on Automatic Control. Vol. 60(3):857–862. 2015. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

One-Click Submission System Now Available for SO Preprints, learn more on how this works in our blog post and don't forget to check the video, too!

Comparing Discrete and Continuous Data: Concepts, Differences, and Applications.

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comment on this article