Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
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DOI：
10.1109/tbdata.2019.2922969
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发表时间：
2019-06
影响因子：
7.2
通讯作者：
Conor Fahy;Shengxiang Yang
中科院分区：
文献类型：
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作者：
Conor Fahy;Shengxiang Yang
变化是动态流挖掘中最大的挑战之一。从数据挖掘的角度来看，适应和跟踪变化是可取的，以了解如何以及为什么发生变化。聚类是一种无监督学习的形式，可用于识别流中的底层模式。基于密度的聚类将聚类识别为由低密度区域分隔的高密度区域。本文提出了一种多密度流聚类（MDSC）算法来解决这两个问题：多密度问题和发现和跟踪动态流中的变化的问题。MDSC由两个在线组件组成：发现，标记的集群和离群值缓冲区。传入的点被分配到活动聚类或传递到离群值缓冲区。新的集群被发现在缓冲区中使用蚂蚁启发的群体智能方法。新发现的集群被唯一地标记并添加到活动集群的集合中。经过处理的数据会受到老化功能的影响，当它不再相关时就会消失。MDSC表现出良好的国家的最先进的同行流聚类算法的范围内的真实的和合成的数据流。实验结果表明，MDSC可以发现定性有用的模式，同时具有可扩展性和鲁棒性的噪声。
Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise.