International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 8, 8 Aug. 2017
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Data Visualization and its Proof by Compactness Criterion of Objects of Classes
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Author(s)
Index Terms
Data visualization, logical regularities, nonlinear mapping, compactness of objects
Abstract
In this paper considered the problem of reducing the dimension of the feature space using nonlinear mapping the object description on numerical axis. To reduce the dimensionality of space used by rules agglomerative hierarchical grouping of different - type (nominal and quantitative) features. Groups do not intersect with each other and their number is unknown in advance. The elements of each group are mapped on the numerical axis to form a latent feature. The set of latent features would be sorted by the informativeness in the process of hierarchical grouping. A visual representation of objects obtained by this set or subset is used as a tool for extracting hidden regularities in the databases. The criterion for evaluating the compactness of the class objects is based on analyzing the structure of their connectivity. For the analysis used an algorithm partitioning into disjoint classes the representatives of the group on defining subsets of boundary objects. The execution of algorithm provides uniqueness of the number of groups and their member objects in it.
The uniqueness property is used to calculate the compactness measure of the training samples. The value of compactness is measured with dimensionless quantities in the interval of [0, 1]. There is a need to apply of dimensionless quantities for estimating the structure of feature space. Such a need exists at comparing the different metrics, normalization methods and data transformation, selection and removing the noise objects.
Cite This Paper
Saidov Doniyor Yusupovich, "Data Visualization and its Proof by Compactness Criterion of Objects of Classes", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.8, pp.51-58, 2017. DOI:10.5815/ijisa.2017.08.06
Reference
[1]Duke V.A., Methodology of finding the logical patterns in the domain of fuzzy systemology: On the example of clinical and experimental studies, St. Petersburg, 2005.
[2]R. Duda, Hart P., Detection and Scene Analysis, Moscow: Mir, 1976.
[3]Zagoruyko N. G., Kutnenko O. A., Ziryanov A. O., Levanov D. A. Obuchenie raspoznavaniyu obrazov bez pereobucheniya // Mashinnoe obuchenie i analiz dannix. – 2014 . – T. 1 7. – pp. 891-901.
[4]Ignatev N.A. Obobshennie otsenki i lokalnie metriki ob’ektov v intellektualnom analize dannix. – Tashkent: Natsionalniy universitet Uzbekistana im. Mirzo Ulugbeka, 2014.
[5]H. Liu and H. Motoda, Feature selection for knowledge discovery and data mining, vol. 454, Springer Science & Business Media, 2012.
[6]I. A. Gheyas and L. S. Smith, "Feature subset selection in large dimensionality domains," Pattern recognition, vol. 43, no. 1, pp. 5-13, 2010.
[7]N. Kwak and C.-H. Choi, "Input feature selection for classification problems," IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 143-159, 2002.
[8]D. Asir Antony Gnana Singh, E. Jebamalar Leavline, R. Priyanka, P. Padma Priya, Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis, IJISA, vol. 8, №1, 2016, pp. 67-73.
[9]S. Dash,B. Patra, B.K. Tripathy, A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set, IJIEEB vol. 4, №2, 2012, pp. 43-50.
[10]Ignatiev N.A., On the construction of the feature space to search for logical regularities in pattern recognition problems, Computational technologies, vol. 17, №4, 2012, pp. 56-62.
[11]Ignatev N.A. Vichislenie obobshennix otsenok i ierarxicheskaya gruppirovka priznakov. Vestnik Tomskogo gosudarstvennogo universiteta. Tomsk, 2015, pp. 31-38.
[12]Saidov D.Yu., Nonlinear conversion of feature space and its analytical representation, International Youth Scientific Forum "Lomonosov-2015", 2015.
[13]Ignatiev N.A., Cluster analysis and selection of objects standards in pattern recognition problems with the teacher, Computational technologies, vol. 20, № 6, 2015, pp. 34-43.
[14]G. R. Norman, D. L. Streiner, Biostatistics:The Bare Essentials, PMPH-USA, 2008.
[15]http://archive.ics.uci.edu/ml/machine-learning-databases/echocardiogram