Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
The Gauss-Tree: Efficient Object Identification in Databases of Probabilistic Feature Vectors
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ProUD: Probabilistic Ranking in Uncertain Databases
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Efficient Processing of Top-k Queries in Uncertain Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Probabilistic similarity join on uncertain data
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Similarity search and mining in uncertain databases
Proceedings of the VLDB Endowment
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An object o of a database $\mathcal{D}$ is called a hot item , if there is a sufficiently large population of other objects in $\mathcal{D}$ that are similar to o . In other words, hot items are objects within a dense region of other objects and provide a basis for many density-based data mining techniques. Intuitively, objects that share their attribute values with a lot of other objects could be potentially interesting as they show a typical occurrence of objects in the database. Also, there are a lot of application domains, e.g. sensor databases, traffic management or recognition systems, where objects have vague and uncertain attributes. We propose an approach for the detection of potentially interesting objects (hot items ) of an uncertain database in a probabilistic way. An efficient algorithm is presented which detects hot items , where to each object o a confidence value is assigned that reflects the likelihood that o is a hot item . In an experimental evaluation we show that our method can compute the results very efficiently compared to its competitors.