FP-tree Based Spatial Co-location Pattern Mining

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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 ... continued below

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Yu, Ping May 2005.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 366 times , with 4 in the last month . More information about this thesis can be viewed below.

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  • Yu, Ping

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Description

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.

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  • May 2005

Added to The UNT Digital Library

  • Feb. 15, 2008, 4:15 p.m.

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  • Oct. 31, 2013, 4:23 p.m.

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Citations, Rights, Re-Use

Yu, Ping. FP-tree Based Spatial Co-location Pattern Mining, thesis, May 2005; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc4724/: accessed November 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .