Aykırı değer tespitinde yoğunluk tabanlı kümeleme yöntemleri
Density-based clustering methods for outlier detection
- Tez No: 243932
- Danışmanlar: YRD. DOÇ. DR. SONGÜL ALBAYRAK
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2009
- Dil: Türkçe
- Üniversite: Yıldız Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Bilgisayar Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 70
Özet
Fraud that causes high amounts of finance loss, has became one of the serious problems. Either proactive efforts that focuses on prevention of fraud or working on fraud detection always use data mining approaches.Outlier detection, which is one of the data mining studies, detects objects that has different behavior in similar elements. These elements are usually nominated to be fraudulent elements. Clustering methods are mostly used for outlier detection. Clustering algorithms that are sensitive to noise or the inconsistent elements, are playing an active role in the detection of fraudulent behavior.Clustering is one of the data mining methods that is used for the unsupervised analysis of the data. Especially, if the data has not enough information(foreknowledge), similar data is grouped by the help of the clustering methods. DBSCAN, which is the one of the density-based clustering methods, does the process of clustering, according to density of data.Although DBSCAN method seems effective in the small data sets, its efficiency decreases with the growing of data volumes. Because of this reason, DBSCAN as a clustering method is not considered a suitable clustering method for large data sets.In the scope of this thesis, R-P-DBSCAN (Recursive-Partitioned DBSCAN) algorithm is proposed. The new algorithm is based on partitioning & combining and DBSCAN algorithm is used for data clustering. Large-volume data sets are divided into smaller pieces and clustered by DBSCAN. Then, combining each clustered piece, until whole set of data is clustered. Each cluster obtained by R-P-DBSCAN, is the same as the clusters obtained with the classical DBSCAN.The results obtained with R-P-DBSCAN have shown that, the proposed algorithm has better clustering performance (until 85%) according to classical DBSCAN algorithm
Özet (Çeviri)
Fraud that causes high amounts of finance loss, has became one of the serious problems. Either proactive efforts that focuses on prevention of fraud or working on fraud detection always use data mining approaches.Outlier detection, which is one of the data mining studies, detects objects that has different behavior in similar elements. These elements are usually nominated to be fraudulent elements. Clustering methods are mostly used for outlier detection. Clustering algorithms that are sensitive to noise or the inconsistent elements, are playing an active role in the detection of fraudulent behavior.Clustering is one of the data mining methods that is used for the unsupervised analysis of the data. Especially, if the data has not enough information(foreknowledge), similar data is grouped by the help of the clustering methods. DBSCAN, which is the one of the density-based clustering methods, does the process of clustering, according to density of data.Although DBSCAN method seems effective in the small data sets, its efficiency decreases with the growing of data volumes. Because of this reason, DBSCAN as a clustering method is not considered a suitable clustering method for large data sets.In the scope of this thesis, R-P-DBSCAN (Recursive-Partitioned DBSCAN) algorithm is proposed. The new algorithm is based on partitioning & combining and DBSCAN algorithm is used for data clustering. Large-volume data sets are divided into smaller pieces and clustered by DBSCAN. Then, combining each clustered piece, until whole set of data is clustered. Each cluster obtained by R-P-DBSCAN, is the same as the clusters obtained with the classical DBSCAN.The results obtained with R-P-DBSCAN have shown that, the proposed algorithm has better clustering performance (until 85%) according to classical DBSCAN algorithm
Benzer Tezler
- Comperative evaluation of unsupervised fraud detection algorithms with feature extraction and scaling in purchasing domain
Satın alma alanında özellik çıkarma ve ölçekleme ile denetimsiz sahtekarlık tespit algoritmalarının karşılaştırmalı değerlendirmesi
YİĞİT CAN TAŞOĞLU
Yüksek Lisans
İngilizce
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiVeri Analitiği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ MEHMET ALİ ERGÜN
- Unsupervised anomaly detection algorithms
Denetimsiz anomali tespit algoritmaları
BEYZA KIZILKAYA
Yüksek Lisans
İngilizce
2019
İstatistikDokuz Eylül Üniversitesiİstatistik Ana Bilim Dalı
DR. ÖĞR. ÜYESİ ENGİN YILDIZTEPE
- Augmented superpixel based anomaly detection in hyperspectral imagery
Hiperspektral görüntülerde genişletilmiş süperpiksel tabanlı anomali tespiti
EZGİ GÖKDEMİR
Yüksek Lisans
İngilizce
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiHesaplamalı Bilimler ve Mühendislik Ana Bilim Dalı
DR. ÖĞR. ÜYESİ SÜHA TUNA
- Akan verilerde aykırı değer tespiti yaklaşımları
Outlier detection approaches in streaming data
NİHAL CEYHAN
Yüksek Lisans
Türkçe
2019
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolEge ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
DOÇ. DR. HASAN BULUT
- Early diagnosis of acute coronary syndromes automatically by using features of ECG recordings
EKG kayıtlarının öznitelikleri kullanılarak akut koroner sendromların otomatik olarak erken teşhisi
MERVE BEGÜM TERZİ
Yüksek Lisans
İngilizce
2014
Elektrik ve Elektronik Mühendisliğiİhsan Doğramacı Bilkent ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
PROF. DR. ORHAN ARIKAN