Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering
Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-09-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1060.pdf |
| _version_ | 1818029044054097920 |
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| author | Zeynel Cebeci Cagatay Cebeci Yalcin Tahtali Lutfi Bayyurt |
| author_facet | Zeynel Cebeci Cagatay Cebeci Yalcin Tahtali Lutfi Bayyurt |
| author_sort | Zeynel Cebeci |
| collection | DOAJ |
| description | Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets. |
| first_indexed | 2024-12-10T05:13:25Z |
| format | Article |
| id | doaj.art-3e0ba5409b764335bdb460074d391912 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-10T05:13:25Z |
| publishDate | 2022-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-3e0ba5409b764335bdb460074d3919122022-12-22T02:01:02ZengPeerJ Inc.PeerJ Computer Science2376-59922022-09-018e106010.7717/peerj-cs.1060Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clusteringZeynel Cebeci0Cagatay Cebeci1Yalcin Tahtali2Lutfi Bayyurt3Department of Animal Science, Faculty of Agriculture, Cukurova University, Adana, TurkeyDepartment of Electronics & Electrical Engineering, University of Strathclyde, Glasgow, United KingdomDepartment of Agriculture, Faculty of Agriculture, Tokat Gaziosmanpasa University, Tokat, TurkeyDepartment of Agriculture, Faculty of Agriculture, Tokat Gaziosmanpasa University, Tokat, TurkeyOutliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.https://peerj.com/articles/cs-1060.pdfOutlier detectionAnomaly detectionUnsupervised learningFuzzy and possibilistic clusteringData analysis |
| spellingShingle | Zeynel Cebeci Cagatay Cebeci Yalcin Tahtali Lutfi Bayyurt Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering PeerJ Computer Science Outlier detection Anomaly detection Unsupervised learning Fuzzy and possibilistic clustering Data analysis |
| title | Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| title_full | Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| title_fullStr | Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| title_full_unstemmed | Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| title_short | Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| title_sort | two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| topic | Outlier detection Anomaly detection Unsupervised learning Fuzzy and possibilistic clustering Data analysis |
| url | https://peerj.com/articles/cs-1060.pdf |
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