Jagiellonian University Repository

KNAC : an approach for enhancing cluster analysis with background knowledge and explanations


KNAC : an approach for enhancing cluster analysis with background knowledge and explanations

Show full item record

dc.contributor.author Bobek, Szymon [SAP14044690] pl
dc.contributor.author Kuk, Michał pl
dc.contributor.author Brzegowski, Jakub pl
dc.contributor.author Brzychczy, Edyta pl
dc.contributor.author Nalepa, Grzegorz [SAP14004063] pl
dc.date.accessioned 2023-05-15T14:10:37Z
dc.date.available 2023-05-15T14:10:37Z
dc.date.issued 2023 pl
dc.identifier.issn 0924-669X pl
dc.identifier.uri https://ruj.uj.edu.pl/xmlui/handle/item/311349
dc.language eng pl
dc.rights Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/legalcode.pl *
dc.title KNAC : an approach for enhancing cluster analysis with background knowledge and explanations pl
dc.type JournalArticle pl
dc.description.physical 15537-15560 pl
dc.description.additional Online First 2022-11-23 pl
dc.abstract.en Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KNAC). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, KNAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation. pl
dc.subject.en data mining pl
dc.subject.en explainable AI pl
dc.subject.en clustering pl
dc.description.volume 53 pl
dc.description.number 12 pl
dc.identifier.doi 10.1007/s10489-022-04310-9 pl
dc.identifier.eissn 1573-7497 pl
dc.title.journal Applied Intelligence pl
dc.language.container eng pl
dc.affiliation Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej pl
dc.subtype Article pl
dc.rights.original CC-BY; inne; ostateczna wersja wydawcy; w momencie opublikowania; 0 pl
dc.identifier.project NCN 2018/27/Z/ST6/03392 pl
dc.identifier.project U1U/P06/NO/02.16 pl
dc.pbn.affiliation Dziedzina nauk inżynieryjno-technicznych : informatyka techniczna i telekomunikacja pl
.pointsMNiSW [2023 A]: 70

Files in this item

This item appears in the following Collection(s)

Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa Except where otherwise noted, this item's license is described as Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa