Impact of Clustering Parameters on the Efficiency of the Knowledge Mining Process in Rule-based Knowledge Bases

• Autorzy: Nowak-Brzezińska A. , Rybotycki T.

Identyfikatory

DOI

10.4467/20838476SI.16.007.6188

• Języki publikacji: EN

Abstrakty

EN

In this work the subject of the application of clustering as a knowledge extraction method from real-world data is discussed. The authors analyze an influence of different clustering parameters on the quality of the created structure of rules clusters and the efficiency of the knowledge mining process for rules / rules clusters. The goal of the experiments was to measure the impact of clustering parameters on the efficiency of the knowledge mining process in rulebased knowledge bases denoted by the size of the created clusters or the size of the representatives. Some parameters guarantee to produce shorter/longer representatives of the created rules clusters as well as smaller/greater clusters sizes.

Słowa kluczowe

EN

rule-based knowledge bases; clustering; similarity; visualization

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2016
• Tom: Vol. 25
• Strony: 85--101
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Nowak-Brzezińska A.

• University of Silesia, ul. Bankowa 12 40-007 Katowice, Poland

autor

Rybotycki T.

• BS PAN, Doctoral Study, ul. Newelska 6 01-447 Warszawa, Poland

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).