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Association rule mining is one of the most exploited areas in data mining, which includes applications from business to data classification and summarization. Thus, several association rule-based c...

We present a high-speed planning method with compact precomputed search trees using a new pruning method and evaluate the effectiveness and the efficiency of our precomputation planning. Its speed is faster than an A* planner in maps in which the obstacle rate is the same as indoor environments. Precomputed search trees are one way of reducing planning time; however, there is a time-memory trade off. Our precomputed search tree (PCS) is built with pruning based on a rule of constant memory, the maximum size pruning method (MSP) which is a preset ratio of pruning. Using MSP, we get a large precomputed search tree which is a reasonable size. Additionally, we apply the node selection strategy (NSS) to MSP. We extend the outer edge of the tree and enhance the path reachability. In maps less than 30% obstacle rates on a map, the runtime of precomputation planning is more than one order of magnitude faster than the planning without precomputed search trees. Our precomputed tree finds an optimal path in maps with 25% obstacle rates. Then our precomputation planning speedily produces the optimal path in indoor environments.

The article presents an algorithm that combines Minimax's alpha-beta pruning with IDA*'s depth-first iteration and forward pruning. We prove that the algorithm, which we call Two - agent IDA* (TIDA*), is correct and is guaranteed to terminate—provided its heuristic is consistent. Minimax search has been applied with other ‘forward’ pruning or selective search techniques, but none of these techniques is guaranteed to compute the correct minimax result. We also prove that TIDA*'s accuracy monotonically increases with search depth, thus avoiding certain pathologies. Empirical results with 3×3, 4×4 and 5×5 Tic-Tac-Tff grids show that TIDA* outperforms MAB, provided both algorithms use the same consistent heuristic. However, results on a more complex search space—the 3D version of Tic-Tac-Tff—show that MAB with a good inconsistent heuristic can outperform TIDA* with a consistent heuristic even though TIDA* searches 1 to 1·5 levels deeper. This result and other empirical results suggest that an algorithm that s...