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

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 proce...

Full description

Saved in:
Bibliographic Details
Published inSchedae Informaticae (Print) Vol. 25; p. 85
Main Authors Nowak-Brzezińska, Agnieszka, Rybotycki, Tomasz
Format Journal Article
LanguageEnglish
Published Kraków Jagiellonian University-Jagiellonian University Press 2017
Subjects
Algorithms
Cluster analysis
Clustering
Clusters
Computer science
Data mining
Efficiency
Experts
Influence
Knowledge base
Knowledge representation
Similarity measures
Software
Trends
Visualization
Online AccessGet full text
ISSN1732-3916
2083-8476
2083-8476
DOI10.4467/20838476SI.16.007.6188

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1732-3916
2083-8476
2083-8476
DOI:10.4467/20838476SI.16.007.6188