FP-tree Based Spatial Co-location Pattern Mining

A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.

Grantor dc:publisher

University of North Texas

Year dc:date

2005

Author dc:creator

• Yu, Ping

Contributors dc:contributor

• Huang, Yan
• Mikler, Armin R.
• Brazile, Robert

• Data mining.
• Pattern perception.
• FP-CM
• frequent patterns
• FP-tree
• co-location pattern
• spatial databases
• spatial data mining

Statement dc:rights

• Public
• Copyright
• Yu, Ping
• Copyright is held by the author, unless otherwise noted. All rights reserved.

Language dc:language

English

Identifier

oclc: 62161034
https://digital.library.unt.edu/ark:/67531/metadc4724/
ark: ark:/67531/metadc4724

OAI identifier oai:identifier

info:ark/67531/metadc4724