Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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
Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A unified approach for mining outliers
CASCON '97 Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier Detection Based on Voronoi Diagram
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
| Hi-index | 0.00 |
An important problem in database and data mining systems is the detection of outlying points. It is often the case that data observations exhibiting atypical properties are of more interest than those fitting common patterns. While anomaly and outlier detection have received considerable attention from the statistics community, these approaches are primarily focused on analysis of data sets containing relatively few and univariate observations. Recently, valuable approaches have been proposed to facilitate multidimensional analysis for larger data sets. Unfortunately, these approaches are often expensive and require numerous comparisons between each point and the remainder of the data. We propose an approach using histograms for outlier detection. Sparse regions of the data are recognised and used for identifying points that are likely to be outliers. An extensive experimental evaluation demonstrates the efficiency of our approach under a number of circumstances with varying parameters on real world and synthetic data sets.