التفاصيل البيبلوغرافية
| العنوان: |
Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering |
| المؤلفون: |
Zeynel Cebeci, Cagatay Cebeci, Yalcin Tahtali, Lutfi Bayyurt |
| المصدر: |
PeerJ Computer Science, Vol 8, p e1060 (2022) |
| بيانات النشر: |
PeerJ Inc., 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: |
Outlier detection, Anomaly detection, Unsupervised learning, Fuzzy and possibilistic clustering, Data analysis, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2376-5992 |
| Relation: |
https://peerj.com/articles/cs-1060.pdf; https://peerj.com/articles/cs-1060/; https://doaj.org/toc/2376-5992 |
| DOI: |
10.7717/peerj-cs.1060 |
| URL الوصول: |
https://doaj.org/article/3e0ba5409b764335bdb460074d391912 |
| رقم الانضمام: |
edsdoj.3e0ba5409b764335bdb460074d391912 |
| قاعدة البيانات: |
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