Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Incremental Maintenance of Discovered Spatial Colocation Patterns
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Mining At Most Top-K% Mixed-drove Spatio-temporal Co-occurrence Patterns: A Summary of Results
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Discovering spatial interaction patterns
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
How to use "classical" tree mining algorithms to find complex spatio-temporal patterns?
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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The research tracks the spread of co-occurrence phenomena over the zonal space. Spread patterns of spatio-temporal co-occurrences over zones (SPCOZs) represent the spread structures over the zones for the subsets of features whose events co-locate in space and time. SPCOZs are of great use in many applications, such as tracking the evolutions of infectious diseases and ecological disasters in space and time. However, finding SPCOZs is computationally expensive due to large size of history data sets, exponential number of feature combinations, and complex interest measures. In this paper, we propose a novel Spread Pattern Tree (SP-Tree) to index the spread elements of the SPCOZs which holds the monotonic property with the size of the co-occurrences. We also propose an efficient mining algorithm (SPCOZ-Miner) for mining SPCOZs. The experimental evaluation with both synthetic and real-world data sets shows our algorithm is effective and much more efficient than a straight approach.