Adaptive evolutionary clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term t...

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Bibliographic Details
Published inData mining and knowledge discovery Vol. 28; no. 2; pp. 304 - 336
Main Authors Xu, Kevin S., Kliger, Mark, Hero III, Alfred O.
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.03.2014
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Chemistry and Earth Sciences
Clustering
Computer Science
Data Mining and Knowledge Discovery
Data smoothing
Datasets
Estimates
Experiments
Information Storage and Retrieval
Noise
Physics
Signal processing
Statistics for Engineering
Evolutionary clustering
Tracking
Similarity measures
Clustering algorithms
Adaptive filtering
Shrinkage estimation
Data smoothing
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Summary:In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.
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ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-012-0302-x