Incomplete Information in Relational Databases
Journal of the ACM (JACM)
On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Algorithms for clustering data
Algorithms for clustering data
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Information Retrieval
An Evidential Reasoning Approach to Attribute Value Conflict Resolution in Database Integration
IEEE Transactions on Knowledge and Data Engineering
Modeling Uncertainty in Databases
Proceedings of the Seventh International Conference on Data Engineering
An Extended Relational Database Model for Uncertain and Imprecise Information
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
GADT: A Probability Space ADT for Representing and Querying the Physical World
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based clustering of uncertain data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Hierarchical Density-Based Clustering of Uncertain Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Challenges for Data Mining in Distributed Sensor Networks
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fuzzy Databases: Modeling, Design, and Implementation
Fuzzy Databases: Modeling, Design, and Implementation
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
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
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
Kernel based K-medoids for clustering data with uncertainty
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Uncertain centroid based partitional clustering of uncertain data
Proceedings of the VLDB Endowment
DAGger: clustering correlated uncertain data (to predict asset failure in energy networks)
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
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Uncertain data are usually represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In the context of uncertain data management, there has been a growing interest in clustering uncertain data. In particular, the classic K-means clustering algorithm has been recently adapted to handle uncertain data. However, the centroid-based partitional clustering approach used in the adapted K-means presents two major weaknesses that are related to: (i)an accuracy issue, since cluster centroids are computed as deterministic objects using the expected values of the pdfs of the clustered objects; and, (ii)an efficiency issue, since the expected distance between uncertain objects and cluster centroids is computationally expensive.In this paper, we address the problem of clustering uncertain data by proposing a K-medoids-based algorithm, called UK-medoids, which is designed to overcome the above issues. In particular, our UK-medoids algorithm employs distance functions properly defined for uncertain objects, and exploits a K-medoids scheme. Experiments have shown that UK-medoids outperforms existing algorithms from an accuracy viewpoint while achieving reasonably good efficiency.