Energy-based function to evaluate data stream clustering

Marcelo Albertini () and Rodrigo Mello ()

Advances in Data Analysis and Classification, 2013, vol. 7, issue 4, 435-464

Abstract: Severe constraints imposed by the nature of endless sequences of data collected from unstable phenomena have pushed the understanding and the development of automated analysis strategies, such as data clustering techniques. However, current clustering validation approaches are inadequate to data streams due to they do not properly evaluate representation of behavior changes. This paper proposes a novel function to continuously evaluate data stream clustering inspired in Lyapunov energy functions used by techniques such as the Hopfield artificial neural network and the Bidirectional Associative Memory ( Bam). The proposed function considers three terms: i) the intra-cluster distance, which allows to evaluate cluster compactness; ii) the inter-cluster distance, which reflects cluster separability; and iii) entropy estimation of the clustering model, which permits the evaluation of the level of uncertainty in data streams. A first set of experiments illustrate the proposed function applied to scenarios of continuous evaluation of data stream clustering. Further experiments were conducted to compare this new function to well-established clustering indices and results confirm our proposal reflects the same information obtained with external clustering indices. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Machine learning; Data streams; Data clustering; Evaluation of data clustering; Lyapunov energy function; 62H30; 68T10; 91C20; 68T05 (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11634-013-0145-3

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