Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection
Outliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier...
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| Format: | Article |
| Language: | English |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2024-05-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
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| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/25450 |
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| author | Annisa Putri Utami Anwar Fitrianto Khairil Anwar Notodiputro |
| author_facet | Annisa Putri Utami Anwar Fitrianto Khairil Anwar Notodiputro |
| author_sort | Annisa Putri Utami |
| collection | DOAJ |
| description | Outliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier detection methods/techniques were first pioneered by researchers in statistics. However, due to rapid technological advances which have an impact on the ease of collecting extensive data, the development of outlier detection techniques is now handled mainly by researchers in the field of computer science (data mining) using computing facilities. This research aims to examine the results of simulation studies by comparing methods for identifying several outliers using statistical approaches and data mining algorithm approaches in various predetermined data scenarios. Based on the scenario carried out, the outlier detection method using a statistical approach is generally better than the outlier detection method using a data mining-based approach. Suggestions for further research are to improve the data mining method by focusing more on statistical analysis apart from focusing on data processing computing time so that the expected results of outlier detection are faster and more precise. |
| format | Article |
| id | doaj-art-be03888a9f8f4be9bdf72eec3231da3b |
| institution | OA Journals |
| issn | 2086-0382 2477-3344 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj-art-be03888a9f8f4be9bdf72eec3231da3b2025-08-20T02:12:46ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442024-05-019111912810.18860/ca.v9i1.254507813Comparison between Statistical Approaches and Data Mining Algorithms for Outlier DetectionAnnisa Putri Utami0Anwar Fitrianto1Khairil Anwar Notodiputro2Department of Statistics, IPB UniversityDepartment of Statistics, IPB UniversityDepartment of Statistics, IPB UniversityOutliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier detection methods/techniques were first pioneered by researchers in statistics. However, due to rapid technological advances which have an impact on the ease of collecting extensive data, the development of outlier detection techniques is now handled mainly by researchers in the field of computer science (data mining) using computing facilities. This research aims to examine the results of simulation studies by comparing methods for identifying several outliers using statistical approaches and data mining algorithm approaches in various predetermined data scenarios. Based on the scenario carried out, the outlier detection method using a statistical approach is generally better than the outlier detection method using a data mining-based approach. Suggestions for further research are to improve the data mining method by focusing more on statistical analysis apart from focusing on data processing computing time so that the expected results of outlier detection are faster and more precise.https://ejournal.uin-malang.ac.id/index.php/Math/article/view/25450distance-based methodsmaskingoutlieroutlier detection methodswamping |
| spellingShingle | Annisa Putri Utami Anwar Fitrianto Khairil Anwar Notodiputro Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection Cauchy: Jurnal Matematika Murni dan Aplikasi distance-based methods masking outlier outlier detection method swamping |
| title | Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection |
| title_full | Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection |
| title_fullStr | Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection |
| title_full_unstemmed | Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection |
| title_short | Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection |
| title_sort | comparison between statistical approaches and data mining algorithms for outlier detection |
| topic | distance-based methods masking outlier outlier detection method swamping |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/25450 |
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