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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Objective Clustering Inductive Technology of Gene Expression Sequences Features
verfasst von : Sergii Babichev, Volodymyr Lytvynenko, Maxim Korobchynskyi, Mochamed Ali Taiff
Erschienen in: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation
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Abstract
Technology of high dimensional data features objective clustering based on the methods of complex systems inductive modeling is presented in the paper. Architecture of the objective clustering inductive technology as a block diagram of step-by-step implementation of the objects clustering procedure was developed. Method of criterial evaluation of complex data clustering results using two equal power data subsets is proposed. Degree of clustering objectivity evaluates on the basis of complex use of internal and external criteria. Researches on the simulation results of the proposed technology based on the SOTA self-organizing clustering algorithm using the gene expression data obtained by DNA microarray analysis of patients with lung cancer GEOD-68571 Array Express database, the datasets “Compound” and “Aggregation” of the Computing School of the Eastern Finland University and the data “seeds” are presented.