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中文摘要：
本文提出了两种正态云模型相似度计算方法,分别通过正态云模型的期望曲线和最大边界曲线来描述正态云模型的总体特征,实现以期望曲线相似程度或最大边界曲线的相似程度对正态云模型相似度的定量表示.它们在一定程度上克服了传统基于特征向量和随机选取云滴的相似度计算带来云模型期望数字特征过于显著、时间复杂度过高和结果不稳定等方面的不足.实验结果表明,本文算法能够更为客观地对正态云模型进行相似度计算,在协同过滤推荐以及时间序列分类中得到了应用并提高了算法的效率.
英文摘要：
We proposed two methods to measure the similarity of normal cloud models. One uses the expectation curves to reflect the overall feature of cloud models and to calculate the similarity by the expectation curves. The other uses the maximum boundary curve to compute the similarity between different clouds. The two methods can obtain a qualitative result, which overcomes the traditional deficiencies of the high time complexity, unstable result and excessively remarkable expectation character. The experi- mental results demonstrate that our methods can calculate the similarity of cloud models objectively and improve the efficiency of the algorithms in collaborative filtering recommendation and time series classification.
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