Bibliografia

Bibliografia#

[AMMIL12]

Yaser S Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from data. Volume 4. AMLBook New York, 2012.

[CalinskiH74]

Tadeusz Caliński and Jerzy Harabasz. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1–27, 1974.

[Cic07]

Paweł Cichosz. Systemy uczące się. Wydawnictwa Naukowo-Techniczne, 2007.

[Dom12]

Pedro Domingos. A few useful things to know about machine learning. Communications of the ACM, 55(10):78–87, 2012.

[Fla12]

Peter Flach. Machine learning: the art and science of algorithms that make sense of data. Cambridge university press, 2012.

[GBC16]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. book in preparation for mit press. URL¡ http://www. deeplearningbook. org, 2016.

[HTFF09]

Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction. Volume 2. Springer, 2009.

[Jai10]

Anil K Jain. Data clustering: 50 years beyond k-means. Pattern recognition letters, 31(8):651–666, 2010.

[LW14]

Dao Lam and Donald Wunsch. Clustering, pages 1115–1149. Volume 1. NJ, United States: Wiley–IEEE Press, 12 2014. doi:10.1016/B978-0-12-396502-8.00020-6.

[Lea97]

Machine Learning. Tom mitchell. Publisher: McGraw Hill, 1997.

[MN07]

Tengyu Ma and Andrew Ng. Cs229 lecture notes. In CS229 Lecture notes. 2007. URL: https://api.semanticscholar.org/CorpusID:628573.

[MRTB12]

M Mohri, A Rostamizadeh, A Talwalkar, and F Bach. Foundations of Machine Learning. MIT Press, 2012. ISBN 9780262018258.

[Rou87]

Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65, 1987.

[SW17]

Claude Sammut and Geoffrey I Webb. Encyclopedia of machine learning and data mining. Springer Publishing Company, Incorporated, 2017.

[Sim83]

Herbert A Simon. Why should machines learn? In Machine learning, pages 25–37. Elsevier, 1983.

[Woj21]

Filip Wójcik. Supporting decision processes in the organization with the use of associative analysis algorithms. PhD thesis, Wroclaw University of Economics and Buisiness, Wrocław, Poland, November 2021. Available at https://wir.ue.wroc.pl/info/phd/UEWR3483636aa91d4d2d81133ea64251c351/.