A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series

Massa, Silvia; Paolucci, Massimo; Puliafito, P. P.

doi:10.1007/3-540-48298-9_35

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In our paper we construct a general cross-entropy clustering (CEC) theory which simultaneously joins, and even overcomes, the clustering advantages of 1 The disadvantages of common clustering methods are excellently summarized in the third paragraph of [13]: "[...] The weaknesses of k-MEANS result in poor quality clustering, and thus, more statistically sophisticated alternatives have been proposed. [...] While these alternatives offer more statistical accuracy, robustness and less bias, they trade this for substantially more computational requirements and more detailed prior knowledge [14]."…”

Section: Motivationmentioning

confidence: 99%

Cross-entropy clustering

Tabor

Spurek

2014

Pattern Recognition

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We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which carry no information.Although CEC, similarly like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is affine invariant, while the numerical complexity is comparable to that of k-means.

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Section: Motivationmentioning

confidence: 99%

Cross-entropy clustering

Tabor

Spurek

2014

Pattern Recognition

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“…The relation between the above two methods is well described by Estivill-Castro and Yang (2000): "[...] The weaknesses of k-means results in poor quality clustering, and thus, more statistically sophisticated alternatives have been proposed. [...] While these alternatives offer more statistical accuracy, robustness and less bias, they trade this for substantially more computational requirements and more detailed prior knowledge, see Massa, Paolucci, and Puliafito (1999). "…”

Section: Introductionmentioning

confidence: 99%

Introduction to Cross-Entropy Clustering The R Package CEC

Tabor¹,

Spurek²,

Kamieniecki³

et al. 2015

Preprint

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The R Package CEC Kamieniecki and Spurek (2014) performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory. The main advantage of CEC is that it combines the speed and simplicity of k-means with the ability to use various Gaussian mixture models and reduce unnecessary clusters. In this work we present a practical tutorial to CEC based on the R Package CEC. Functions are provided to encompass the whole process of clustering.

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“…Representatives of these alternatives are Expectation Maximization (and model-based clustering [6,14,24]), Data Augmentation [46] and Gibbs sampling Markov chain Monte Carlo algorithms [3,26,45]. While these alternatives offer more statistical accuracy, robustness and less bias, they trade this for substantially more computational requirements and more detailed prior knowledge [36].…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust General Purpose Clustering Algorithms

Estivill‐Castro

Yang

2004

Data Mining and Knowledge Discovery

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General purpose and highly applicable clustering methods are usually required during the early stages of knowledge discovery exercises. k-Means has been adopted as the prototype of iterative model-based clustering because of its speed, simplicity and capability to work within the format of very large databases. However, k-Means has several disadvantages derived from its statistical simplicity. We propose an algorithm that remains very efficient, generally applicable, multidimensional but is more robust to noise and outliers. We achieve this by using medians rather than means as estimators for the centers of clusters. Comparison with k-Means, Expectation Maximization and Gibbs sampling demonstrates the advantages of our algorithm.

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A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series

Cited by 3 publications

References 8 publications

Cross-entropy clustering

Cross-entropy clustering

Introduction to Cross-Entropy Clustering The R Package CEC

Fast and Robust General Purpose Clustering Algorithms

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