Abstract
Data from sensor logs in raw form is generally continuous valued. This data from multiple sensors in continuous stream becomes voluminous. For knowledge discovery like extraction of context, from these datasets, standard machine learning algorithms or their variations are used as classifier. Most classification schemes require the input data to be discretized. The focus of this paper is to study merits of some popular discretization methods when applied on noisy sensor logs. Representative methods from supervised and unsupervised discretization, like binning, clustering and entropy minimization are evaluated with context extraction. Interestingly, unlike common perception, for discretization of sensor data, supervised algorithms do not have a clear edge over unsupervised.
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Mittal, S., Gopal, K., Maskara, S.L. (2014). Effect of Choice of Discretization Methods on Context Extraction from Sensor Data – An Empirical Evaluation. In: Natarajan, R. (eds) Distributed Computing and Internet Technology. ICDCIT 2014. Lecture Notes in Computer Science, vol 8337. Springer, Cham. https://doi.org/10.1007/978-3-319-04483-5_16
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DOI: https://doi.org/10.1007/978-3-319-04483-5_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04482-8
Online ISBN: 978-3-319-04483-5
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