2020
DOI: 10.3390/e22101096
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Exploration of Outliers in If-Then Rule-Based Knowledge Bases

Abstract: The article presents both methods of clustering and outlier detection in complex data, such as rule-based knowledge bases. What distinguishes this work from others is, first, the application of clustering algorithms to rules in domain knowledge bases, and secondly, the use of outlier detection algorithms to detect unusual rules in knowledge bases. The aim of the paper is the analysis of using four algorithms for outlier detection in rule-based knowledge bases: Local Outlier Factor (LOF), Connectivity-based Out… Show more

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Cited by 5 publications
(3 citation statements)
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“…In work [9], we examined 7 different measures of cluster quality assessment. They included both measures, which, while improving the quality of clusters, reduce their baseline value and act inversely.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In work [9], we examined 7 different measures of cluster quality assessment. They included both measures, which, while improving the quality of clusters, reduce their baseline value and act inversely.…”
Section: Resultsmentioning
confidence: 99%
“…It's worth recalling that the clustering flow can be different depending on the clustering method we used. Hence, in our research, we use several options 1 so that we can always find the optimal solution [9].…”
Section: Clustering and Outlier Mining In Rulesmentioning
confidence: 99%
“…We want our solution to be universal and, therefore, to work effectively both for data with outliers and for typical data. We also want our solution to be effective for any data type, not just only numerical, which is easier to analyze [ 2 , 3 , 4 ].…”
Section: Introductionmentioning
confidence: 99%