. . . "22532" . "RIV/68407700:21240/14:00219641!RIV15-MSM-21240___" . "The main objective of outlier detection is find- ing samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover in- teresting events within data. Consequently, a considerable amount of statistical and data mining techniques to iden- tify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was la- belled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as con- junctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by hu- mans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anoma- lies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our ap- proach is demonstrated on several synthetic and one real world datasets."@en . "Kopp, Martin" . "Pevn\u00FD, Tom\u00E1\u0161" . . "The main objective of outlier detection is find- ing samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover in- teresting events within data. Consequently, a considerable amount of statistical and data mining techniques to iden- tify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was la- belled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as con- junctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by hu- mans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anoma- lies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our ap- proach is demonstrated on several synthetic and one real world datasets." . "RIV/68407700:21240/14:00219641" . . . "Anomaly detection; anomaly interpretation; clustering; decision trees; feature selection; random forest"@en . . "21240" . . . . . "I, P(GA13-17187S), P(GPP103/12/P514), S" . . . "Proceedings of the 14th conference ITAT 2014 \u2013 Workshops and Posters" . "http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=88442135003" . . "Interpreting and clustering outliers with sapling random forests"@en . "[E3E607563AE6]" . "Interpreting and clustering outliers with sapling random forests"@en . . "1"^^ . "2014-09-25+02:00"^^ . . "3"^^ . "Praha" . "Interpreting and clustering outliers with sapling random forests" . "\u00DAstav informatiky AV \u010CR" . "Dem\u00E4novsk\u00E1 Dolina" . "Hole\u0148a, Martin" . "978-80-87136-19-5" . "Interpreting and clustering outliers with sapling random forests" . . . . . . . "7"^^ .