The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Learning Based Neural Similarity Metrics for Multimedia Data Mining
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Clustering Uncertain Data Using Voronoi Diagrams
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Naive Bayes Classification of Uncertain Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Decision Trees for Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Experiments with random projection
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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
Distance-based feature selection on classification of uncertain objects
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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We study the problem of classification on uncertain objects whose locations are uncertain and described by probability density functions (pdf). Though there exist some classification algorithms proposed to handle uncertain objects, all existing algorithms are complex and time consuming. Thus, a novel supervised UK-means algorithm is proposed to classify uncertain objects more efficiently. Supervised UK-means assumes the classes are well separated. However, in real data, subsets of objects of the same class are usually interspersed among (disconnected by) other classes. Thus, we proposed a new algorithm Supervised UK-means with Multiple Subclasses (SUMS) which considers the objects in the same class can be further divided into several groups (subclasses) within the class and tries to learn the subclass representatives to classify objects more accurately.