Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering
Yazarlar (4)
Zeynel Cebeci Çukurova Üniversitesi, Türkiye
Çağatay Cebeci University Of Strathclyde, Türkiye
Doç. Dr. Yalçın TAHTALI Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Dr. Öğr. Üyesi Lütfi BAYYURT Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Peerj Computer Science (Q3) | ||
| Dergi ISSN | 2376-5992 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2022 |
| Cilt / Sayı / Sayfa | 8 / 1 / – | DOI | 10.7717/PEERJ-CS.1060 |
| Makale Linki | https://peerj.com/articles/cs-1060/ | ||
| Özet |
| 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. |
| Anahtar Kelimeler |
| Anomaly detection | Data analysis | Fuzzy and possibilistic clustering | Outlier detection | Unsupervised learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 13 |
| Scopus | 5 |
| Web of Science | 6 |