Algorithms for clustering data
Algorithms for clustering data
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
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
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Approximation algorithms for clustering uncertain data
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Uncertain Data Via K-Medoids
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
A Hierarchical Algorithm for Clustering Uncertain Data via an Information-Theoretic Approach
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index
IEEE Transactions on Knowledge and Data Engineering
Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects
ICDM '10 Proceedings of the 2010 IEEE International Conference on 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
| Hi-index | 0.00 |
Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitional clustering has been particularly studied and a number of algorithms have been developed. While existing proposals differ mainly in the notions of cluster centroid and clustering objective function, little attention has been given to an analysis of their characteristics and limits. In this work, we theoretically investigate major existing methods of partitional clustering, and alternatively propose a well-founded approach to clustering uncertain data based on a novel notion of cluster centroid. A cluster centroid is seen as an uncertain object defined in terms of a random variable whose realizations are derived based on all deterministic representations of the objects to be clustered. As demonstrated theoretically and experimentally, this allows for better representing a cluster of uncertain objects, thus supporting a consistently improved clustering performance while maintaining comparable efficiency with existing partitional clustering algorithms.