LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
Knowledge and Information Systems
A genetic approach for efficient outlier detection in projected space
Pattern Recognition
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Projected outlier detection in high-dimensional mixed-attributes data set
Expert Systems with Applications: An International Journal
Detecting outlying properties of exceptional objects
ACM Transactions on Database Systems (TODS)
Information Sciences: an International Journal
Discovering unexpected documents in corpora
Knowledge-Based Systems
Variable-precision dominance-based rough set approach and attribute reduction
International Journal of Approximate Reasoning
A comparison of outlier detection algorithms for ITS data
Expert Systems with Applications: An International Journal
Semi-supervised outlier detection based on fuzzy rough C-means clustering
Mathematics and Computers in Simulation
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
The distribution of test statistics for outlier detection in heavy-tailed samples
Mathematical and Computer Modelling: An International Journal
Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking
Knowledge-Based Systems
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
Advances in Engineering Software
A novel soft set approach in selecting clustering attribute
Knowledge-Based Systems
PCA-based high-dimensional noisy data clustering via control of decision errors
Knowledge-Based Systems
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Detection of outlier from high dimensional dataset have found important applications in many fields, yet the unexpected time consumption is likely to hinder its practical use. Thus, it makes sense to build an efficient method for finding meaningful outliers and analyzing their intentional knowledge. In this paper, we utilize the concept of rough set to construct a method for outlying reduction, based on an outlier detection and analysis system. By defining outlying partition similarity, we can mine outliers on the key attribute subset rather than on the full dimensional attribute set of dataset, as long as the similarity between outlying partitions produced on them is large enough. For this purpose, we propose a novel method for finding the key attribute subset in dataset, which starts by seeking all outliers on the full attribute set, and then searches through all outlying attribute subsets for these points. After that, it turns out to be able to determine the key attribute subset in accordance with the similarity between outlying partitions. By experiments, we show that our method allows more efficient seeking of key attribute subset than the previous methods, thereby improving the feasibility of outlier detection.