Authors
Marwan Hassani, Philipp Kranen, Thomas Seidl
Publication date
2011/8/21
Book
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Pages
52-60
Description
Clustering of streaming sensor data aims at providing online summaries of the observed stream. This task is mostly done under limited processing and storage resources. This makes the sensed stream speed (data per time) a sensitive restriction when designing stream clustering algorithms. Additionally, the varying speed of the stream is a natural characteristic of sensor data, e.g. changing the sampling rate upon detecting an event or for a certain time. In such cases, most clustering algorithms have to heavily restrict their model size such that they can handle the minimal time allowance. Recently the first anytime stream clustering algorithm has been proposed that flexibly uses all available time and dynamically adapts its model size. However, the method was not designed to precisely cluster sensor data which are usually noisy and extremely evolving. In this paper we detail the LiarTree algorithm that provides …
Total citations
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Scholar articles
M Hassani, P Kranen, T Seidl - Proceedings of the Fifth International Workshop on …, 2011