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Symmetry 2016, 8(12), 152; https://doi.org/10.3390/sym8120152

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

1
Group Bio-Process Analysis Technology, Technische Universität München, Weihenstephaner Steig 20, 85354 Freising, Germany
2
Bavarian State Research Center for Agriculture, Institute for Agricultural Engineering and Animal Husbandry, Vöttinger Straße 36, 85354 Freising, Germany
3
Computer Unit, Faculty of Agriculture, University of Ruhuna, Mapalana, Kamburupitiy 81100, Sri Lanka
4
Lehrstuhl für Brau- und Getränketechnologie, Technische Universität München, Weihenstephaner Steig 20, 85354 Freising, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Doo-Soon Park and Shu-Ching Chen
Received: 8 June 2016 / Revised: 7 November 2016 / Accepted: 5 December 2016 / Published: 14 December 2016
(This article belongs to the Special Issue Scientific Programming in Practical Symmetric Big Data)
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Abstract

Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 224 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method. View Full-Text
Keywords: big data; clustering; contour lines; data and knowledge visualization; knowledge retrieval; mining methods and algorithms; missing data; real-time systems big data; clustering; contour lines; data and knowledge visualization; knowledge retrieval; mining methods and algorithms; missing data; real-time systems
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Adikaram, K.; Hussein, M.A.; Effenberger, M.; Becker, T. Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets. Symmetry 2016, 8, 152.

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