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Spatial Separation of Molecular Conformers and Clusters
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The Information Bottleneck and Geometric Clustering.

D J Strouse1, David J Schwab2

  • 1Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A. danieljstrouse@gmail.com.

Neural Computation
|October 14, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a new geometric clustering method using the deterministic information bottleneck (DIB). This approach offers an information-theoretic perspective, generalizing classic algorithms like k-means and Gaussian Mixture Models.

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Area of Science:

  • Machine Learning
  • Information Theory
  • Data Science

Background:

  • Classic clustering algorithms like k-means and GMMs rely on geometric distances.
  • The Information Bottleneck (IB) approach clusters data based on preserving information about a relevant variable.
  • A gap exists in applying information-theoretic principles directly to geometric clustering problems.

Purpose of the Study:

  • To adapt the deterministic information bottleneck (DIB) for geometric clustering.
  • To develop an intuitive method for selecting the optimal number of clusters.
  • To provide an information-theoretic foundation for existing clustering techniques.

Main Methods:

  • Utilizing the deterministic information bottleneck (DIB) to cluster data points based on location.
  • Developing a model selection criterion using 'kinks' in the information curve to determine cluster count.
  • Applying the DIB approach to various synthetic clustering datasets.

Main Results:

  • The DIB method successfully recovers underlying cluster labels in synthetic data.
  • The proposed model selection method effectively identifies the correct number of clusters.
  • Under specific parameter limits, DIB clustering demonstrates equivalence to k-means and Gaussian Mixture Model (GMM) fitting.

Conclusions:

  • Clustering with DIB offers a generalized information-theoretic framework for geometric clustering.
  • This approach provides new insights into the relationship between information theory and classic clustering algorithms.
  • The DIB method, coupled with the proposed cluster selection, presents a powerful alternative for geometric clustering tasks.