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中文摘要：
在许多数据流采矿应用程序，传统的密度评价方法象内核密度评价那样，因为他们的高计算的负担，减少的集合密度评价不能被用于数据流的密度评价，处理时间和集中的存储器分配要求。以便减少时空复杂性，在数据上的新奇密度评价方法 Dm-KDE 基于能被用来与核部件的固定数字设计一个 KDE 评估者的建议算法 m-KDE 流因为数据集被建议。在这个方法， Dm-KDE 顺序条目被算法 m-KDE 创造而不是从另外的密度评价方法获得的所有核。为了推进，减少存储空间， Dm-KDE 顺序条目能被计算他们的 KL 分叉合并。最后，在任意的时间的概率密度功能或全部时间能通过获得的评价模型被估计。与最先进的算法 SOMKE 相对照，建议算法 Dm-KDE 的特殊优点存在因为它能与内核部件的更不固定的数字完成一样的精确性以便它对关于在数据流上的内核密度评价的更高联机的计算是 required.We 的情形合适把 Dm-KDE 与 SOMKE 和M核作比较以为各种各样的静止数据集的密度评价精确性和运行时间。我们也把 Dm-KDE 用于发展数据溪流。试验性的结果说明建议方法的有效性。
英文摘要：
In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.
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