Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 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
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
LDBOD: A novel local distribution based outlier detector
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
A methodology for handling a new kind of outliers present in gene expression patterns
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
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
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Quick spatial outliers detecting with random sampling
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
| Hi-index | 0.10 |
Outlier detection targets those exceptional data that deviate from the general pattern. Besides high density clustering, there is another pattern called low density regularity. Thus, there are two types of outliers w.r.t. them. We propose two techniques: one to identify the two patterns and the other to detect the corresponding outliers.