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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Clustering Models and Applications
Fuzzy Clustering Models and Applications
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Density-based clustering of uncertain data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Hierarchical Density-Based Clustering of Uncertain Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining Uncertain Data in Low-dimensional Subspace
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Clustering Uncertain Data Using Voronoi Diagrams
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Boosting spatial pruning: on optimal pruning of MBRs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index
IEEE Transactions on Knowledge and Data Engineering
Probabilistic similarity join on uncertain data
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Uncertain data mining: an example in clustering location data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Speeding-Up hierarchical agglomerative clustering in presence of expensive metrics
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Preserving user location privacy in mobile data management infrastructures
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Uncertain centroid based partitional clustering of uncertain data
Proceedings of the VLDB Endowment
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We study the problem of clustering data objects with location uncertainty. In our model, a data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster such uncertain objects is to apply the UK-means algorithm [1], an extension of the traditional K-means algorithm, which assigns each object to the cluster whose representative has the smallest expected distance from it. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration of the pdf. We study two pruning methods: pre-computation (PC) and cluster shift (CS) that can significantly reduce the number of integrations computed. Both pruning methods rely on good bounding techniques. We propose and evaluate two such techniques that are based on metric properties (Met) and trigonometry (Tri). Our experimental results show that Tri offers a very high pruning power. In some cases, more than 99.9% of the expected distance calculations are pruned. This results in a very efficient clustering algorithm.