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'''Data-driven astronomy''' ('''DDA)''') refers to the use of [[data science]] in [[astronomy]]. Several outputs of [[Telescopic observational astronomy|telescopic observations]] and [[Astronomical survey|sky surveys]] are taken into consideration and approaches related to [[data mining]] and big data management are used to analyze, filter, and [[Normalization (statistics)|normalize]] the [[data set]] that are further used for making Classifications, Predictions, and Anomaly detections by [[Advances in Statistics|advanced Statistical approaches]], [[digital image processing]] and [[machine learning]]. The output of these processes is used by [[astronomer]]s and space scientists to study and identify patterns, anomalies, and movements in outer space and conclude theories and discoveries in the [[cosmos]].
== History ==
=== Classification ===
'''''Classification'''''<ref>{{Cite book |last1=Chowdhury |first1=Shovan |last2=Schoen |first2=Marco P. |chapter=Research Paper Classification using Supervised Machine Learning Techniques |date=2020-10-02 |title=2020 Intermountain Engineering, Technology and Computing (IETC) |chapter-url=https://ieeexplore.ieee.org/document/9249211 |publisher=IEEE |pages=1–6 |doi=10.1109/IETC47856.2020.9249211 |isbn=978-1-7281-4291-3}}</ref> is used for specific identifications and categorizations of astronomical data such as [[Stellar classification|Spectral classification]], Photometric classification, Morphological classification, and classification of [[Solar phenomena|solar activity]]. The approaches of classification techniques are listed below:
* [[Artificial neural network]] (ANN)
=== Regression ===
'''''Regression'''''<ref>{{Citation |last1=Sarstedt |first1=Marko |title=Regression Analysis |date=2014 |work=A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics |pages=193–233 |editor-last=Sarstedt |editor-first=Marko |url=https://doi.org/10.1007/978-3-642-53965-7_7 |access-date=2024-05-10 |place=Berlin, Heidelberg |publisher=Springer |language=en |doi=10.1007/978-3-642-53965-7_7 |isbn=978-3-642-53965-7 |last2=Mooi |first2=Erik |editor2-last=Mooi |editor2-first=Erik}}</ref> is used to make predictions based on the retrieved data through statistical trends and statistical modeling. Different uses of this technique are used for fetching [[Photometric redshift]]s and measurements of physical parameters of stars.<ref>{{Cite journal |title=Bulletin de la Société Royale des Sciences de Liège {{!}} PoPuPS |url=https://popups.uliege.be/0037-9565/index.php |journal=Bulletin de la Société Royale des Sciences de Liège |language=fr |issn=0037-9565}}</ref> The approaches are listed below:
* [[Artificial neural network]] (ANN)
=== Clustering ===
'''''Clustering'''''<ref>{{Cite book |last1=Bindra |first1=Kamalpreet |last2=Mishra |first2=Anuranjan |chapter=A detailed study of clustering algorithms |date=September 2017 |title=2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) |chapter-url=https://ieeexplore.ieee.org/document/8342454 |publisher=IEEE |pages=371–376 |doi=10.1109/ICRITO.2017.8342454 |isbn=978-1-5090-3012-5}}</ref> is classifying objects based on a [[similarity measure]] metric. It is used in Astronomy for Classification as well as [[Object detection|Special/rare object detection]]. The approaches are listed below:
* [[Principal component analysis]] (PCA)
=== Anomaly detection ===
'''''Anomaly detection'''''<ref>{{Cite journal |last1=Thudumu |first1=Srikanth |last2=Branch |first2=Philip |last3=Jin |first3=Jiong |last4=Singh |first4=Jugdutt (Jack) |date=2020-07-02 |title=A comprehensive survey of anomaly detection techniques for high dimensional big data |journal=Journal of Big Data |volume=7 |issue=1 |pages=42 |doi=10.1186/s40537-020-00320-x |doi-access=free |issn=2196-1115|hdl=10536/DRO/DU:30158643 |hdl-access=free }}</ref> is used for detecting irregularities in the dataset. However, this technique is used here to detect [[Object detection|rare/special objects]]. The following approaches are used:
* [[Principal component analysis|Principal Component Analysis (PCA)]]
=== Time-series analysis ===
'''''Time-Series analysis'''''<ref>{{Cite book |url=https://onlinelibrary.wiley.com/doi/book/10.1002/0471264385 |title=Handbook of Psychology |date=2003-04-15 |publisher=Wiley |isbn=978-0-471-17669-5 |editor-last=Weiner |editor-first=Irving B. |edition=1 |language=en |doi=10.1002/0471264385.wei0223}}</ref> helps in analyzing trends and predicting outputs over time. It is used for trend prediction and novel detection (detection of unknown data). The approaches used here are:
* [[Artificial neural network]] (ANN)
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