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Clustering Da ta Streams Based on Shared Density between Micro-Clusters

Clustering Da ta Streams Based on Shared Density between Micro-Clusters

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Clustering Da ta Streams Based on Shared Density between Micro-Clusters

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 da ta 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 re cluster the micro-clusters into larger final clusters. For re clustering, 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 re clustering is based on possibly inaccurate assumptions about the distribution of da ta within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly


 


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  • Model: PROJ7529
  • 999 Units in Stock
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This product was added to our catalog on Saturday 29 July, 2017.

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