Trend analysis of categorical data streams with a concept change method
Trend analysis of categorical data streams with a concept change method
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DOI：
10.1016/j.ins.2014.02.052
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Fuyuan Cao;J. Huang;Jiye Liang
中科院分区：
文献类型：
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作者：
Fuyuan Cao;J. Huang;Jiye Liang
本文提出了一种新的分类数据流趋势分析方法。数据流被划分为一系列时间窗口，并且假设每个窗口中的记录携带许多表示为簇的概念。提出了一种数据标记算法，用于从前一个窗口的概念中识别窗口的概念或簇。给出概念的表达，并定义两个连续窗口中两个概念之间的距离，以分析连续窗口中概念的变化。最后，提出了一种趋势分析算法来计算连续时间窗口序列上数据流中概念变化的趋势。提出了测量引起概念变化的属性显着性和对象离群度的方法，以揭示概念变化的原因。在真实数据集上进行的实验证明了趋势分析方法的好处。
This paper proposes a new method to trend analysis of categorical data streams. A data stream is partitioned into a sequence of time windows and the records in each window are assumed to carry a number of concepts represented as clusters. A data labeling algorithm is proposed to identify the concepts or clusters of a window from the concepts of the preceding window. The expression of a concept is presented and the distance between two concepts in two consecutive windows is defined to analyze the change of concepts in consecutive windows. Finally, a trend analysis algorithm is proposed to compute the trend of concept change in a data stream over the sequence of consecutive time windows. The methods for measuring the significance of an attribute that causes the concept change and the outlier degrees of objects are presented to reveal the causes of concept change. Experiments on real data sets are presented to demonstrate the benefits of the trend analysis method.