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Temporal Structure Learning for Clustering Massive Data Streams in Real-Time
[chapter]
2011
Proceedings of the 2011 SIAM International Conference on Data Mining
This paper describes one of the first attempts to model the temporal structure of massive data streams in real-time using data stream clustering. Recently, many data stream clustering algorithms have been developed which efficiently find a partition of the data points in a data stream. However, these algorithms disregard the information represented by the temporal order of the data points in the stream which for many applications is an important part of the data stream. In this paper we propose
doi:10.1137/1.9781611972818.57
dblp:conf/sdm/HahslerD11
fatcat:qtq3jk7yezg2xlcni4nj2xxeqq