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Co-location pattern mining (for CSCI 5715) Charandeep Parisineti, Bhavtosh Rath Chapter 7: Spatial Data Mining [1]Yan Huang, Shashi Shekhar, Hui Xiong. Discovering Co-location patterns from Spatial Datasets: A General Approach. IEEE Transactions on Knowledge and Data Engineering, 2004. [2]Sajib Barua, Jörg Sander. Mining Statistically Significant Co-location and Segregation Patterns. IEEE Transactions on Knowledge and Data Engineering, 26(5), 2014. [3] Shashi Shekar, et al., Trends in Spatial Data Mining Where do we find Co –location? In ecology Symbiotic species : Ox-pecker and giraffe Public Safety To determine possible causes of disease outbreak (London cholera) In cities {‘auto dealers’, ‘auto repair shops’} {‘departmental stores’, ’gift stores’} Other domains: Earth science, public safety, transportation, tourism etc. What are association rules? Association rules vs Co-location rules Class Exercise: Co-location rule mining approaches b) Reference feature centric approach c) Data partition approach d) Event centric model • Co-location (A,B) will not be found in (b) since it does not involve the reference feature • Data partition approach can have many distinct ways of partitioning the data, each yielding a distinct set of transactions and hence support. In (c) support of (A,B) is different for different partitions Event centric model finds the subsets of spatial features likely to occur in a neighborhood around instances of given subsets of event types Event Centric model Drawbacks of event centric model • In (a) there are only a few instances of A but B is abundant. As many Bs are without As B’s participation ratio will be small which results the participation index of {A,B} to be low • In (b) A and B are abundant in a spatial area but randomly distributed We might see enough instances of {A,B} even without true spatial dependency • In (c) both features A and B are spatially auto-correlated. A cluster of A and a cluster of B happen to overlap by chance. Enough instances of {A,B} will falsely report a spatial co-location Statistical approaches Similar problems as above in association rule mining are handled using Interest measures such as phi coefficient for a 2x2 contingency table Coffee ~Coffee Tea 15 5 ~Tea 75 5 ~Coffee means the transactions which don’t contain coffee The absence of an item doesn’t make sense in spatial data as boolean spatial features are embedded in continuous space Hence phi coefficient, odds ratio etc, used in traditional data mining don’t work for Spatial data The basic idea in Spatial statistical approach is comparing the observed PI value to a PI value under no spatial relationship instead of a global threshold The process is repeated over several spaces with CSR using Monte Carlo simulation Computing co-location patterns using cross k function for all possible co-location patterns can be computationally expensive Q?