Outliers in rules - the comparision of LOF, COF and KMEANS algorithms. | Zendy

Agnieszka Nowak-Brzezińska | Zendy; Czesław Horyń | Zendy

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Outliers in rules - the comparision of LOF, COF and KMEANS algorithms.

Author(s) -

Agnieszka Nowak-Brzezińska,

Czesław Horyń

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.09.152

Subject(s) - outlier , computer science , anomaly detection , data mining , k means clustering , local outlier factor , quality (philosophy) , domain (mathematical analysis) , cluster (spacecraft) , algorithm , pattern recognition (psychology) , artificial intelligence , cluster analysis , mathematics , mathematical analysis , philosophy , epistemology , programming language

The aim of the article is the analysis of using LOF, COF and Kmeans algorithms for outlier detection in rule based knowledge bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently.

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