An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts
An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts
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DOI：
10.1109/tkde.2016.2594068
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发表时间：
2016-11
影响因子：
8.9
通讯作者：
Liang Bai;Xueqi Cheng;Jiye Liang;Huawei Shen
中科院分区：
文献类型：
--
作者：
Liang Bai;Xueqi Cheng;Jiye Liang;Huawei Shen
在分类数据流上，一直缺乏一个聚类有效性函数和优化策略来发现聚类和捕捉聚类结构的演化趋势。因此，本文提出了一个分类数据流聚类的优化模型。在该模型中，提出了一个聚类有效性函数作为目标函数，以评估每个新的输入数据子集流动时聚类模型的有效性。该算法在聚类过程中同时考虑了聚类模型的确定性和与上一个聚类模型的连续性。提出了一种迭代优化算法来求解具有约束条件的目标函数的最优解。此外，我们严格推导出检测指标漂移的概念，从优化模型。我们提出了一种检测方法，结合检测指标和优化模型，捕捉分类数据流上的簇结构的演变趋势。该方法能有效避免忽略聚类有效性对检测结果的影响。最后，通过对几个真实的数据集的实验研究，与已有的数据流聚类算法进行了比较，验证了该算法在分类数据流聚类中的有效性.
There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the clustering model while each new input data subset is flowing. It simultaneously considers the certainty of the clustering model and the continuity with the last clustering model in the clustering process. An iterative optimization algorithm is proposed to solve an optimal solution of the objective function with some constraints. Furthermore, we strictly derive a detection index for drifting concepts from the optimization model. We propose a detection method that integrates the detection index and the optimization model to catch the evolution trend of cluster structures on a categorical data stream. The new method can effectively avoid ignoring the effect of the clustering validity on the detection result. Finally, using the experimental studies on several real data sets, we illustrate the effectiveness of the proposed algorithm in clustering categorical data streams, compared with existing data-streams clustering algorithms.