A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy the normalization conditions for different problems. A hybrid algorithm based on the modified PSO ...
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A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy the normalization conditions for different problems. A hybrid algorithm based on the modified PSO and combining forecasting is presented. Combining forecasting can improve the forecasting accuracy through combining different forecasting methods. The effectiveness of the algorithm is demonstrated through the prediction on the sunspots and the stocks data. Simulated results show that the hybrid algorithm can improve the forecasting accuracy to a great extent.
The problem of mining frequent patterns plays an essential role in many important data mining tasks. However, it often generates a very large number of frequent itemsets. The set of frequent closed itemsets uniquely d...
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ISBN:
(纸本)0780384032
The problem of mining frequent patterns plays an essential role in many important data mining tasks. However, it often generates a very large number of frequent itemsets. The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. An algorithm Closearcher based on formal concept analysis for closed itemset searching is proposed. This algorithm divides the whole search space of closed itemsets into several subspaces in accordance with criterions prescribed ahead, and introduces an efficient scheme to recognize the valid ones, in which the search for closed itemsets is bounded. An intermediate structure is employed to judge the validity of a subspace and search closed itemsets more efficiently. The algorithm is experimental evaluated and compared with the famous NextClosure algorithm proposed by Ganter for random generated data, as well as for real application data. The results show that our algorithm performs much better than the later.
Occlusion relation is the topological relation between the images of two bodies from a viewpoint. Qualitative representation of occlusion relation has been investigated in qualitative spatial reasoning. This research ...
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ISBN:
(纸本)0780378652
Occlusion relation is the topological relation between the images of two bodies from a viewpoint. Qualitative representation of occlusion relation has been investigated in qualitative spatial reasoning. This research is important for computer vision and robot navigation. The previous models such as LOS and ROC-20 are all based on RCC (the famous topological theory). But those models couldn't support abstract objects such as point and line which are very common in real applications. To deal with this, multi-dimensional spatial occlusion relation (MSO) is put forward. The foundation of MSO is MRCC which is the multi-dimensional extension of RCC. So MSO is suitable for both real and abstract objects. The conception neighborhood and composition of MSO is given. Finally MSO is extended to spatio-temporal relation by adding time feature. MSO is an appropriate frame to express spatio-temporal knowledge.
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