Clustering Data Streams Based on Shared Density between Micro-Clusters

Michael Hahsler

doi:10.1109/TKDE.2016.2522412

2016-06-01

111

PRE

Michael Hahsler *

DOI：10.1109/TKDE.2016.2522412

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摘要

En 中文

As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to recluster the microclusters into larger final clusters. For reclustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between adjacent micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller microclusters to achieve comparable results.

Keyword:

Data mining

data stream clustering

density-based clustering

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期刊

IEEE Transactions on Knowledge and Data Engineering

IF:

10.4

论文数:

6.8K

被引数:

3.2W

机构

Southern Methodist University

学者数:

3.0K

论文数: 3.5K

被引数: 3.9K

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