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Finding Patterns

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Guide to Intelligent Data Analysis

Part of the book series: Texts in Computer Science ((TCS))

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Abstract

This chapter introduces a variety of methods that are useful to get an overview of the data, which includes a summary of the whole database as well as the identification of areas that exceptionally deviate from the remainder. They provide answers to questions such as: Does it naturally subdivide into groups? How do attributes depend on each other? Are there certain conditions leading to exceptions from the average behaviour? The chapter provides an overview of clustering methods (hierarchical clustering, k-Means, density-based clustering), association analysis, self-organizing maps and deviation analysis. The definition and choice of distance or similarity measures, which is required by almost every technique to compare different cases in the database, is also tackled.

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Notes

  1. 1.

    Isotropic means that the density is symmetric around the mean, that is, the cluster shapes are roughly hyperspheres. Using the Mahalanobis distance instead would allow for ellipsoidal shape, but we restrict ourselves to the isotropic case here.

  2. 2.

    The node are usually called neurons, since self-organizing maps are a special form of neural network.

  3. 3.

    As n is relatively large, we approximate the binomial distribution by a normal distribution. We use the z-test for reasons of simplicity—typically p 0 is not known but has to be estimated from the sample, and then Student’s t-test is used.

  4. 4.

    We will discuss the use of other types of distance metrics in KNIME later.

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Authors and Affiliations

  1. FB Informatik und Informationswissenschaft, Universität Konstanz, 78457, Konstanz, Germany

    Prof. Dr. Michael R. Berthold

  2. Intelligent Data Analysis & Graphical Models Research Unit, European Centre for Soft Computing, C/ Gonzalo Gutiérrez Quirós s/n Edificio Científico-Technológico Campus Mieres, 3ª Planta, 33600, Mieres, Asturias, Spain

    Dr. Christian Borgelt

  3. FB Wirtschaft, Ostfalia University of Applied Sciences, Robert-Koch-Platz 10-14, 38440, Wolfsburg, Germany

    Prof. Dr. Frank Höppner

  4. FB Informatik, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, 38302, Wolfenbüttel, Germany

    Prof. Dr. Frank Klawonn

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  1. Prof. Dr. Michael R. Berthold
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  2. Dr. Christian Borgelt
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  3. Prof. Dr. Frank Höppner
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  4. Prof. Dr. Frank Klawonn
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Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. (2010). Finding Patterns. In: Guide to Intelligent Data Analysis. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-84882-260-3_7

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  • DOI: https://doi.org/10.1007/978-1-84882-260-3_7

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