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The Quasi-circular Mapping Visualization Based on Extending and Reordering Dimensions for Visual Clustering Analysis

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11064))

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Abstract

Radial coordinate visualization (RadViz) and Star Coordinates (SC) can effectively map high dimensional data to low dimensional space, owing to which can place an arbitrary number of Dimension Anchors (DAs). Nevertheless, the problem owner is faced with ordering DAs, which is a NP-complete problem and visual results of crowding which hamper clustering analysis. We introduce a new radial layout visualization, called the Quasi-circular mapping visualization (QCMV), to address those problems in this paper. Firstly, QCMV extend the original dimension of datasets by the probability distribution histogram of the dimension and affinity propagation (AP) algorithm. In additional, distributing them on the unit circle by their correlation according to the correlation of the extended dimensions. Then, mapping the dimensions extended and reordered data to integrate a polygon in the Quasi-circular space and visualizing them by the geometric center and area of the polygon in the three dimension. Finally strengthening their visual clustering effect with t-SNE. We also compare the visual clustering results of RadViz, SC and QCMV with two indexes, correct rate and Dunn index on visually analyzing the three datasets. It shows better effect of visual clustering with QCMV.

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Authors and Affiliations

  1. National Key Laboratory of Image Processing and Pattern Recognition in Jiangxi, Nanchang Hangkong University, Nanchang, 330063, Jiangxi, China

    Ming Li & Hao Chen

  2. School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, Jiangxi, China

    Shan Huang

Authors
  1. Shan Huang
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  2. Ming Li
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  3. Hao Chen
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Corresponding author

Correspondence to Hao Chen .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Huang, S., Li, M., Chen, H. (2018). The Quasi-circular Mapping Visualization Based on Extending and Reordering Dimensions for Visual Clustering Analysis. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_27

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  • DOI: https://doi.org/10.1007/978-3-030-00009-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00008-0

  • Online ISBN: 978-3-030-00009-7

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