CURE: an efficient clustering algorithm for large databases
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
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Generating Network-Based Moving Objects
Geoinformatica
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
A Framework for Outlier Mining in RFID data
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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Outlier mining, also called outlier detection, is a challenging research issue in data mining with important applications as intrusion detection, fraud detection and medical analysis. From the perspective of data, previous work on outlier mining have involved in various types of data such as spatial data, time series data, trajectory data, and sensor data. However, few of them have considered a constrained spatial networks data in which each object must reside or move along a certain edge. In fact, in such special constrained spatial network data environments, previous outlier definitions and the according mining algorithms could work neither properly nor efficiently. In this paper we introduce a new definition of density-based local outlier in constrained spatial networks that considers for each object the outlier-ness with respect to its k nearest neighbors. Moreover , to detect outliers efficiently, we propose a fast cluster-and-bound algorithm that first cluster on each individual edge, then estimate the outlying degree of each cluster and prune those that could not contain top-n outliers, therefore constraining the computation of outliers to only very limited objects. Experiments on synthetic data sets demonstrate the scalability, effectiveness and efficiency of our methods.