Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
IEEE Transactions on Knowledge and Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Mining temporal co-orientation pattern from spatio-temporal databases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
The pattern next door: towards spatio-sequential pattern discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Spatio-temporal databases offer a rich repository and opportunities to develop techniques for discovering new types of spatio-temporal patterns. In this paper, we introduce a new class of spatio-temporal patterns, called the generalized spatio-temporal patterns, to describe the repeated sequences of events that occur within small neighbourhoods. Such patterns are crucial to the understanding of habitual patterns. To discover this class of patterns, we develop an algorithm GenSTMiner based on the idea of pattern growth approach, and introduce some optimization techniques that are used to reduce the number of candidates generated and minimize the size of the projected databases. Our performance study indicates that GenSTMiner is highly efficient and outperforms PrefixSpan.