Abstract: The invention provides a method, apparatus and algorithm for data processing that allows for hypothesis generation and the quantitative evaluation of its validity. The core procedure of the method is the construction of a hypothesis-parameter, acting as an “ego” of the non-biological reasoning system. A hypothesis-parameter may be generated either based on totality of general knowledge facts as a global description of data, or by a specially designed “encapsulation” technique providing for generation of hypothesis-parameters in unsupervised automated mode, after which a hypothesis-parameter is examined for concordance with a totality of parameters describing objects under analysis. The hypothesis examination (verification) is done by establishing a number of copies of a hypothesis-parameter that may adequately compensate for the rest of existing parameters so that the clustering could rely on a suggested hypothesis-parameter.

Abstract: This invention provides a method, apparatus and algorithm for compact description of objects in high-dimensional space of attributes for the purpose of cluster analysis by method of evolutionary transformation of similarity matrices. The proposed method comprises computation of monomeric similarity matrices based on each of parameters that describe a set of objects and the following hybridization of monomeric matrices into a hybrid similarity matrix, which allows for comparison of different attributes on a dimensionless basis. Individual monomeric matrices may be added to a hybrid matrix in any proportion, thus allowing for evaluation of significance of individual parameters. Two types of metrics are proposed for computation of monomeric matrices, depending on quantitative and qualitative nature of attributes used for description of objects under analysis.

Abstract: This invention provides a method, apparatus and algorithm for compact description of objects in high-dimensional space of attributes for the purpose of cluster analysis by method of evolutionary transformation of similarity matrices. The proposed method comprises computation of monomeric similarity matrices based on each of parameters that describe a set of objects and the following hybridization of monomeric matrices into a hybrid similarity matrix, which allows for comparison of different attributes on a dimensionless basis. Individual monomeric matrices may be added to a hybrid matrix in any proportion, thus allowing for evaluation of significance of individual parameters. Two types of metrics are proposed for computation of monomeric matrices, depending on quantitative and qualitative nature of attributes used for description of objects under analysis.

Abstract: The invention provides a method, apparatus and algorithm for data processing that allows for hypothesis generation and the quantitative evaluation of its validity. The core procedure of the method is the construction of a hypothesis-parameter, acting as an “ego” of the non-biological reasoning system. A hypothesis-parameter may be generated either based on totality of general knowledge facts as a global description of data, or by a specially designed “encapsulation” technique providing for generation of hypothesis-parameters in unsupervised automated mode, after which a hypothesis-parameter is examined for the concordance with a totality of parameters describing objects under analysis. The hypothesis examination (verification) is done by establishing a number of copies of a hypothesis-parameter that may adequately compensate for the rest of existing parameters so that the clustering could rely on a suggested hypothesis-parameter.

Abstract: A method for automated hierarchical clustering as a result of simulation of a similarity matrix evolution by the use of an algorithm for matrix transformation inducing a cooperative exchange of information within the entire pool of matrix components and resulting in retrieval of non-obvious information from the underlying data. Hierarchical clustering of matrix components is carried out by iterative transformation and division followed by transformation and division of each level clusters, or by monitoring the changes in individual binary similarities throughout transformation of the entire matrix. For monitoring of changes occurring upon matrix evolution, matrix attenuation technique is applied through the use of the mechanism of contrasting, which permits to pinpoint similarity value changes within a range of thousandths of percent.