Corrections to Bierstone's Algorithm for Generating Cliques
Journal of the ACM (JACM)
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient probabilistic reverse nearest neighbor query processing on uncertain data
Proceedings of the VLDB Endowment
Finding Probabilistic Prevalent Colocations in Spatially Uncertain Data Sets
IEEE Transactions on Knowledge and Data Engineering
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A co-location pattern represents a subset of spatial features whose events tend to locate together in spatial proximity. The certain case of the co-location pattern has been investigated. However, location information of spatial features is often imprecise, aggregated, or error prone. Because of the continuity nature of space, over-counting is a major problem. In the uncertain case, the problem becomes more challenging. In this paper, we propose a probabilistic participation index to measure co-location patterns based on the well-known possible world model. To avoid the exponential cost of calculating participation index from all possible worlds, we prove a lemma that allows for instance centric counting, avoids over-counting, and produces the same results as using possible world based counting. We use this property to develop efficient mining algorithms. We observed through both algebraic analysis and extensive experiments that the feature tree based algorithm outperforms uncertain Apriori algorithm by an order of magnitude not only for co-locations of large sizes but also for datasets with high level of uncertainty. This is an important insight in mining uncertainty co-locations.