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A new visualization tool for data mining techniques

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Abstract

Clustering techniques and classification trees are two of the main techniques used in data mining but, at present, there is still a lack of visualization methods for these tools. Many graphs associated with clustering, also with hierarchical clustering, do not give any information about the values of the centroids’ attributes and the relationships among them. In classification trees, graphical procedures can also be developed to help simplify their interpretation and to obtain a better understanding, but more visualization methods to support this tool are needed. This paper presents a novel visualization technique called sectors on sectors (SonS), and an extended version called multidimensional sectors on sectors (MDSonS), for improving the interpretation of several data mining algorithms. These methods are applied for visualizing the results of: (a) hierarchical clustering, which makes it possible to extract all the existing relationships among centroids’ attributes at any hierarchy level; (b) growing hierarchical self-organizing maps (GHSOM), a variant of the well-known self-organizing maps (SOM), by means of which it is possible to visualize, simultaneously, the data information at each hierarchy level compactly and extract relationships among variables; (c) classification trees, in which the SonS is used for representing the input data information for each class presented in each terminal node of a classification tree providing extra information for a better understanding of the problem. These methods are tested by means of several data sets (real and synthetic). The achieved results show the suitability and usefulness of the proposed approaches.

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Notes

  1. http://mloss.org/software/view/469/.

  2. Each variable is scaled between [0, 1] before carrying out the clustering to avoid a biased model. Moreover, the scaling makes the relevance of each variable (represented by the size of the radius) to be independent of its range, that is, the relevance is not higher even though the variable has a higher range. The use of scaled variables guarantees that the radius is relevant to the variable within the cluster.

  3. This is automatically extensible to other measures such as the median, which is a much more adequate prototype measure in the presence of outliers, for instance. Therefore, SonS is not restricted to the use of a particular prototype measure.

  4. As in SonS, each variable is scaled between [0, 1] before carrying out the clustering to avoid a biased model.

  5. http://cran.R-project.org.

  6. http://archive.ics.uci.edu/ml.

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Authors and Affiliations

  1. Electronic Engineering Department, University of Valencia, Av de la Universidad, s/n, 46100, Burjassot, Valencia, Spain

    José M. Martínez-Martínez, Pablo Escandell-Montero, Emilio Soria-Olivas, José D. Martín-Guerrero & Antonio J. Serrano-López

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  1. José M. Martínez-Martínez
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  2. Pablo Escandell-Montero
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  3. Emilio Soria-Olivas
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  4. José D. Martín-Guerrero
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  5. Antonio J. Serrano-López
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Correspondence to José M. Martínez-Martínez.

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Martínez-Martínez, J.M., Escandell-Montero, P., Soria-Olivas, E. et al. A new visualization tool for data mining techniques. Prog Artif Intell 5, 137–154 (2016). https://doi.org/10.1007/s13748-015-0079-4

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