Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Clustering Spatial Data when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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In this paper, we present our research on data mining approaches in the presence of obstacles. Many algorithms have been designed to detect clusters with obstacles in spatial databases. However, few considered to detect clusters and outliers simultaneously and interactively. Here we extend our original research on iterative cluster and outlier detection to study the problem of detecting cluster and outliers iteratively with the presence of obstacles. In many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing.