Abstract
A co-location pattern is a set of spatial features whose objects are frequently located in spatial proximity. Spatial co-location patterns resemble frequent patterns in many aspects. Since its introduction, the paradigm of mining frequent patterns has undergone a shift from a generate-and-test based frequent pattern mining to a projection based frequent pattern mining. However for spatial datasets, the lack of a transaction concept, which is critical in frequent pattern definition and its mining algorithms, makes the similar shift of paradigm in spatial co-location mining very difficult. We investigate a projection based co-location mining paradigm. In particular, we propose a projection based co-location mining framework and an algorithm called FP-CM, for FP-growth Based Co-location Miner. This algorithm only requires a small constant number of database scans. It out-performs the generate-and-test algorithm by an order of magnitude as shown by our preliminary experiment results.
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, Y., Zhang, L., Yu, P. (2005). Can We Apply Projection Based Frequent Pattern Mining Paradigm to Spatial Co-location Mining?. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_83
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DOI: https://doi.org/10.1007/11430919_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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