Algorithm for searching and analyzing abnormal observations of statistical information based on the Arnold – Kolmogorov – Hecht-Nielsen theorem
An algorithm for detecting runouts in statistical information
is proposed in this article. The idea of the algorithm is based
on the property of some neural networks to demonstrate a
large error in examples during training, which are runouts.
For example, if a perceptron-type neural network has a
relatively small number of hidden neurons, and if there are
relatively few runouts in the training sample, then the neural
network usually demonstrates a higher training error after the
training procedure on the examples that are runouts than on
nonrunout examples. However, two extreme cases are
possible. On the one hand, if a neural network has too many
degrees of freedom, it is usually well trained and
demonstrates small values of the training error in all
examples during training, including examples that are
runouts. This is why a neural network with a large number of
hidden neurons is not suitable for detecting runouts. On the
other hand, if a neural network has too few degrees of
freedom, it will demonstrate large values of the error both in
runout examples and in examples that are not runouts after
the training procedure. As such, it is also not suitable for
detecting runouts. According to the proposed algorithm, a
special neural network is designed using the formula obtained
based on the relation derived from the Arnold – Kolmogorov
– Hecht-Nielsen theorem. This special neural network is
designed only for detecting and identifying outliers. Another
neural network is being designed or other analysis methods
are used for further data analysis. The proposed algorithm is
intended for nonlinear subject areas described by small
volumes of statistical samples that do not necessarily satisfy
the normal distribution law. The application of the algorithm
turned out to be efficient in solving a wide range of problems
from various subject areas, such as medicine, economics,
In article features of non-uniform knowledge are considered, and the hypothesis of location on their structure is offered. This hypothesis allows to express work of the corresponding mechanism of a logic conclusion in the verbal form.
A new computer architecture named object-attribute is offered in the article. Computer of the architecture have all necessary properties for Artificial Intelligence: abstraction of data and program, height concurrency, isomorphism of data and program (i.e. possibility of painless changing of program and data structures), training and self-training of computer system, dataflow, integration of data and program, generation of object description from simple description to complex description, implementation of distribute computer system.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.