Mining Maximal Dynamic Spatial Co-Location Patterns

A spatial co-location pattern represents a subset of spatial features whose instances are prevalently located together in a geographic space. Although many algorithms of mining spatial co-location pattern have been proposed, there are still some problems: 1) they miss some meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}), and get the wrong conclusion that the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on prevalent patterns. 2) Since the number of prevalent spatial co-location patterns is very large, the efficiency of existing methods is very low to mine prevalent spatial co-location patterns. Therefore, first, we propose the concept of dynamic spatial co-location pattern that can reflect the dynamic relationships among spatial features. Second, we mine small number of prevalent maximal dynamic spatial co-location patterns which can derive all prevalent dynamic spatial co-location patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial co-location patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial co-location patterns and two pruning strategies. Finally, the effectiveness and efficiency of the method proposed as well as the pruning strategies are verified by extensive experiments over real/synthetic datasets.Comment: 10 pages,7 figure

Combining tabu search and graph reduction to solve the maximum balanced biclique problem

The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains. This paper introduces a novel algorithm, which combines an effective constraint-based tabu search procedure and two dedicated graph reduction techniques. We verify the effectiveness of the algorithm on 30 classical random benchmark graphs and 25 very large real-life sparse graphs from the popular Koblenz Network Collection (KONECT). The results show that the algorithm improves the best-known results (new lower bounds) for 10 classical benchmarks and obtains the optimal solutions for 14 KONECT instances