Clustering online-marketplace customers’ heterogeneous data applying unsupervised learning methods

dc.contributor.author	Hrytsiuk, Kostiantyn
dc.date.accessioned	2023-07-05T13:17:36Z
dc.date.available	2023-07-05T13:17:36Z
dc.date.issued	2022
dc.identifier.citation	Hrytsiuk Kostiantyn. Clustering online-marketplace customers’ heterogeneous data applying unsupervised learning methods. Bachelor Thesis. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2022, 51 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/3907
dc.description.abstract	The commonchallengeinunderstandingtheinsightsfromtheonlinecustomers’ data istheirsegmentation.Sincetheyhavebothcontinuousandcategoricalfea- tures,thereisnostraightforwardwaytoobtainvaluableclustersbuiltonfeaturesof differenttypes. In thiswork,wewanttocompareexistingalgorithmsforclusteringmixeddata and theapplicationofdifferentmethodstomeasurenon-euclidiandistancesbe- tween datapoints.Theeffectivenessofeach"algorithm-distancemeasure"pair will beevaluatedonthereal-lifesubscriptioncustomersdataset.	uk
dc.language.iso	en	uk
dc.title	Clustering online-marketplace customers’ heterogeneous data applying unsupervised learning methods	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk