Fat node leading tree for data stream clustering with density peaks
Fat node leading tree for data stream clustering with density peaks
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DOI：
10.1016/j.knosys.2016.12.025
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Ji Xu;Guoyin Wang;Tianrui Li;Weihui Deng;Guanglei Gou
中科院分区：
文献类型：
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作者：
Ji Xu;Guoyin Wang;Tianrui Li;Weihui Deng;Guanglei Gou
检测任意形状的簇和不断提供新到达项的结果是数据流聚类研究中的两个关键挑战。然而，现有的聚类方法不能同时处理这两个问题。本文采用基于密度峰值的聚类(DPClust)算法来构造引导树(LT)，并以粒计算的方式将其转化为胖节点引导树(FNLT)。FNLT是用于集群的数据流当前状态的一种新的可解释的概要。新的传入数据被快速混合到不断演变的FNLT结构中，因此传入数据的聚类结果可以在运行中传递。在聚类结果递送和新数据到达之间的时间间隔内，将混合数据的FNLT粒度化为具有固定脂肪节点数的新FNLT。当前数据流的FNLT通过混合-颗粒化-衰落机制以实时方式保持。同时，利用每对聚类中心之间的偏序关系和鞅理论来检测变化点。与几种最新的聚类方法相比，该模型具有较高的精度和效率。
Detecting clusters of arbitrary shape and constantly delivering the results for newly arrived items are two critical challenges in the study of data stream clustering. However, the existing clustering methods could not deal with these two problems simultaneously. In this paper, we employ the density peaks based clustering (DPClust) algorithm to construct a leading tree (LT) and further transform it into a fat node leading tree (FNLT) in a granular computing way. FNLT is a novel interpretable synopsis of the current state of data stream for clustering. New incoming data is blended into the evolving FNLT structure quickly, and thus the clustering result of the incoming data can be delivered on the fly. During the interval between the delivery of the clustering results and the arrival of new data, the FNLT with blended data is granulated as a new FNLT with a constant number of fat nodes. The FNLT of the current data stream is maintained in a real-time fashion by the Blending-Granulating-Fading mechanism. At the same time, the change points are detected using the partial order relation between each pair of the cluster centers and the martingale theory. Compared to several state-of-the-art clustering methods, the presented model shows promising accuracy and efficiency.