Algorithms for clustering data
Algorithms for clustering data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
GraphZip: a fast and automatic compression method for spatial data clustering
Proceedings of the 2004 ACM symposium on Applied computing
FAÇADE: a fast and effective approach to the discovery of dense clusters in noisy spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Spatial contextual noise removal for post classification smoothing of remotely sensed images
Proceedings of the 2005 ACM symposium on Applied computing
The role of visualization in effective data cleaning
Proceedings of the 2005 ACM symposium on Applied computing
On efficient mutual nearest neighbor query processing in spatial databases
Data & Knowledge Engineering
Efficient mutual nearest neighbor query processing for moving object trajectories
Information Sciences: an International Journal
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Cluster analysis is a common approach to pattern discovery in spatial databases. While many clustering techniques have been developed, it is still challenging to discover implicit patterns accurately when the data set contains two kinds of noise or outliers: 1) domain-specific noise; 2) noise similar to true data on size, shape, or density. This paper presents a two-step strategy to solve the problem effectively: firstly, groups of data points are separated into different layers according to their sizes and densities; then a layered visualization is provided to the user to separate noise and true data intuitively. Such a strategy not only produces user-desired results but also separates noise and true data accurately. After noise removal, a hierarchical clustering is performed on remaining data to discover natural clusters. The experimental studies on both benchmark data sets and real images show very encouraging results.