An Efficient Incremental Spatial Association Mining to Explore Significant Rule Changes for Altered Spatial Databases

Abstract

So far, many researches on spatial data mining derive from approaches that are originally used in transactional databases. Moreover, those methods are only practicable with non-recurrent objects. However, data in spatial database increase with time and images with a great quantity of recurrent identical spatial objects are more useful and realistic. Therefore, we propose an efficient and effective incremental spatial association mining method for addressing the problem that the infeasible mining methods can not be applied to the spatial databases well. There are three phases in the proposed method. Phase one is the generation of recurrent spatial patterns. We also apply fuzzy theory to refine the spatial association rules such that these rule sets can include semantic. An incremental mining method, FSP-split (Frequent Spatial Pattern-split) is proposed for exploring the frequent spatial patterns in an altered spatial database. This method is fast because it doesn’t require another scan over the whole database while database is updated. The third phase is to detect the changes between spatial association rule sets in two time periods and calculate the degree of change. Further, our method can calculate the degrees of change to discover the significantly changed rules. The variations of the spatial rule changes over time can be provided for decision-making. Experiments are implemented in both transaction databases and spatial databases. The experimental results in transaction databases demonstrate that our proposed incremental mining method, FSP-split, performs efficiently. The experimental results in spatial databases show that the similarity and unexpectedness measures can detect the rule changes at the difference time.