Communications of the ACM
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Discovering cluster-based local outliers
Pattern Recognition Letters
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
IP algorithms in compact rough classification modeling
Intelligent Data Analysis
A fast greedy algorithm for outlier mining
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Outlier detection using rough set theory
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
An optimization model for outlier detection in categorical data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Outlier detection method based on hybrid rough: negative using PSO algorithm
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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An outlier in a dataset is a point or a class of points that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of outliers is important for many applications and has always attracted attention among data mining research community. In this paper, a new method in detecting outlier based on Rough Sets Theory is proposed. The main concept of using the Rough Sets for outlier detection is to discover Non-Reduct from the information system (IS). Non-Reduct is a set of attributes from IS that may contain outliers. It is discovered through the computation of Non-Reduct by defining Indiscernibility matrix modulo (iDMM D) and Indiscernibility function modulo (iDFM D). A measurement called RSetOF (Rough Set Outlier Factor Value) is hereby defined to identify and detect outlier objects. Extensive experiments were conducted where ten benchmark datasets were tested with the proposed method. To evaluate the effectiveness of performance of the proposed method, RSetAlg is compared to the Frequent Pattern (FindFPOF) method. The experimental result reveals that the approach utilised is a good outlier detection method compared to FindFPOF method. Thus, this proposed method has formed a novel and competitive method in outlier detection.