This paper presents a robust WLAV estimator, which remains insensitive to bad measurements even when these are associated with leverage points. Leverage points are evenly distributed in the factor space of multiple regression via linear transformations. These transformations represent a change of coordinates in the state space. The transformed system of measurement equations are then used to obtain the WLAV estimator for the system states. The transformation based WLAV estimator is shown to remain robust in the presence of leverage points by using some test cases.

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Mixed-drovespatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and a novel MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results also show the proposed method is computationally more efficient than naïve alternatives.

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and novel MDCOP mining algorithms. Analytical results show that the proposed algorithms are correct and complete. Experimental results also show that the proposed methods are computationally more efficient than naïve alternatives.

No abstract