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Pregled bibliografske jedinice broj: 958498

Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models


Matković, Krešimir; Abraham, Hrvoje; Jelović, Mario; Hauser, Helwig
Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models // Machine Learning and Knowledge Extraction. CD-MAKE 2017. Lecture Notes in Computer Science, vol 10410 / Andreas HolzingerPeter KiesebergA Min TjoaEdgar Weippl (ur.).
Cham: Springer International Publishing, 2017. str. 199-218 doi:10.1007/978-3-319-66808-6_14


Naslov
Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models

Autori
Matković, Krešimir ; Abraham, Hrvoje ; Jelović, Mario ; Hauser, Helwig

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Machine Learning and Knowledge Extraction. CD-MAKE 2017. Lecture Notes in Computer Science, vol 10410

Urednik/ci
Andreas HolzingerPeter KiesebergA Min TjoaEdgar Weippl

Izdavač
Springer International Publishing

Grad
Cham

Godina
2017

Raspon stranica
199-218

ISBN
978-3-319-66807-9

ISSN
0302-9743

Ključne riječi
Interactive visual data exploration and analysis Local regression models Externalization of analysis results

Sažetak
Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Profili:

Avatar Url Hrvoje Abraham (autor)

Avatar Url Mario Jelović (autor)

Avatar Url Krešimir Matković (autor)

Citiraj ovu publikaciju

Matković, Krešimir; Abraham, Hrvoje; Jelović, Mario; Hauser, Helwig
Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models // Machine Learning and Knowledge Extraction. CD-MAKE 2017. Lecture Notes in Computer Science, vol 10410 / Andreas HolzingerPeter KiesebergA Min TjoaEdgar Weippl (ur.).
Cham: Springer International Publishing, 2017. str. 199-218 doi:10.1007/978-3-319-66808-6_14
Matković, K., Abraham, H., Jelović, M. & Hauser, H. (2017) Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models. U: Andreas HolzingerPeter KiesebergA Min TjoaEdgar Weippl (ur.) Machine Learning and Knowledge Extraction. CD-MAKE 2017. Lecture Notes in Computer Science, vol 10410. Cham, Springer International Publishing, str. 199-218 doi:10.1007/978-3-319-66808-6_14.
@inbook{inbook, year = {2017}, pages = {199-218}, DOI = {10.1007/978-3-319-66808-6\_14}, keywords = {Interactive visual data exploration and analysis Local regression models Externalization of analysis results}, doi = {10.1007/978-3-319-66808-6\_14}, isbn = {978-3-319-66807-9}, issn = {0302-9743}, title = {Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models}, keyword = {Interactive visual data exploration and analysis Local regression models Externalization of analysis results}, publisher = {Springer International Publishing}, publisherplace = {Cham} }

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