Algorithms for clustering data
Algorithms for clustering data
Detection of outliers and robust estimation using fuzzy clustering
Computational Statistics & Data Analysis
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
Knowledge and Information Systems
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
ODDC: outlier detection using distance distribution clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A new and efficient k-medoid algorithm for spatial clustering
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
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Outlier detection is an important task in a wide variety of application areas. In this paper, a proposed method based on fuzzy clustering approaches for outlier detection is presented. We first perform the c-means fuzzy clustering algorithm. Small clusters are then determined and considered as outlier clusters. The rest of outliers (if any) are then detected in the remaining clusters based on temporary removing a point from the data set and recalculating the objective function. If a noticeable change occurred in the Objective Function (OF), the point is considered an outlier. Experimental results show that our method works well. The test results show that the proposed approach gave good results when applied to different data sets.