Uncertainly measures of rough set prediction
Artificial Intelligence
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
ACM Computing Surveys (CSUR)
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
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Some issues about outlier detection in rough set theory
Expert Systems with Applications: An International Journal
Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The information entropy of rough relational databases
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Outlier detection based on rough membership function
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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
Entropy and co-entropy of a covering approximation space
International Journal of Approximate Reasoning
Outlier detection method based on hybrid rough: negative using PSO algorithm
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
| Hi-index | 12.05 |
The information entropy in information theory, developed by Shannon, gives an effective measure of uncertainty for a given system. And it also seems a competing mechanism for the measurement of uncertainty in rough sets. Many researchers have applied the information entropy to rough sets, and proposed different information entropy models in rough sets. Especially, Duntsch et al. presented a well-justified information entropy model for the measurement of uncertainty in rough sets. In this paper, we shall demonstrate the application of this model for the study of a specific data mining problem - outlier detection. By virtue of Duntsch's information entropy model, we propose a novel definition of outliers -IE (information entropy)-based outliers in rough sets. An algorithm to find such outliers is also given. And the effectiveness of IE-based method for outlier detection is demonstrated on two publicly available data sets.