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

Agnieszka Nowak-Brzezińska,

Tomasz Rybotycki

Abstrakt

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: rule-based knowledge bases, clustering, similarity, visualization
References

[1] Mulawka J.J., Systemy Ekspertowe. Wydawnictwo Naukowo-Techniczne,Warszawa, 1996.

 

[2] Latkowski R., Miko lajczyk M., Data decomposition and decision rule joining for classification of data with missing values. In: Rough Sets and Current Trends

in Computing. vol. 3066 of Lecture Notes in Computer Science., Springer Berlin Heidelberg, 2004, pp. 254–263.

 

[3] Morzy T., Eksploracja danych. Metody i algorytmy. Wydawnictwo Naukowe PWN, 2013.

 

[4] Wierzcho´n S.T., K lopotek M.A., Algorithms of Cluster Analysis. Wydawnictwo IPI PAN, Warszawa, 2015.

 

[5] Boriah S., Chandola V., Kumar V., Similarity measures for categorical data: A comparative evaluation. In: Chid Apte, Haesun Park K.W., Zaki M.J., eds.: Proceedings of the 2008 SIAM International Conference on Data Minning, Society for Industrial and Applied Mathematics, 2008, pp. 243–254.

 

[6] Gower J.C., A general coefficient of similarity and some of its properties. Biometrics, 1971, 27, pp. 857–871.

 

[7] Nowak-Brzezi´nska A., Jach T., Wnioskowanie w systemach z wiedz¸a niepewn¸a. In: Studia Informatica. vol. 32 No 2A. Wydawnictwo Politechniki ´Sl¸askiej 2011, pp. 377–389.

 

[8] Jaccard P., tude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Socit Vaudoise des Sciences Naturelles, 1901, 37, pp. 547–579.

 

[9] Nowak-Brzezi´nska A., Mining rule-based knowledge bases inspired by rough set theory. 2016, 148 (no. 1–2), pp. 35–50.

 

[10] Rybotycki, T., Visualization of hierarchical structures in rule-based knowledge bases. March 2015.

 

[11] Nowak-Brzezi´nska, A., Rybotycki T., Visualization of medical rule-based knowledge bases. Journal of Medical Informatics & Technologies, 2015, 24, pp. 91–98.

 

[12] Shneiderman B., Tree visualization with tree-maps: 2-d space-filling approach. 1992, 11, pp. 92–99.

 

[13] Wetzel K., Pebbles – using circular treemaps to visualize disk usage. http://lip.sourceforge.net/ctreemap.html, 2004.

 

[14] Bazan J.G., Szczuka M.S., Wroblewski J., A new version of rough set exploration system. In: Rough Sets and Current Trends in Computing. vol. 2475 of Lecture Notes in Computer Science., Springer Berlin Heidelberg, 2002, pp. 397–404.

 

[15] Lichman M., Machine learning repository. http://archive.ics.uci.edu/ml, 2013 Accessed in October 2016.

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