Метод анализа многомерных временных рядов с использованием корректировки предварительно рассчитанной обратной матрицы: исследование в сравнении с другими методами Data Mining
This volume of Advances in Intelligent Systems and Computing contains papers presented in the main track of IITI 2016, the First International Conference on Intelligent Information Technologies for Industry held in May 16-21 in Sochi, Russia. The conference was jointly co-organized by Rostov State Transport University (Russia) and VŠB – Technical University of Ostrava (Czech Republic) with the participation of Russian Association for Artificial Intelligence (RAAI) and Russian Association for Fuzzy Systems and Soft Computing (RAFSSC). The volume is devoted to practical models and industrial applications related to intelligent information systems. The conference has been a meeting point for researchers and practitioners to enable the implementation of advanced information technologies into various industries. Nevertheless, some theoretical talks concerning the-state-of-the-art in intelligent systems and soft computing are included in the proceedings as well.
Morrill and Valent´ın in the paper “Computational coverage of TLG: Nonlinearity” considered an extension of the Lambek calculus enriched by a so-called “exponential” modality. This modality behaves in the “relevant” style, that is, it allows contraction and permutation, but not weakening. Morrill and Valent´ın stated an open problem whether this system is decidable. Here we show its undecidability. Our result remains valid if we consider the fragment where all division operations have one direction. We also show that the derivability problem in a restricted case, where the modality can be applied only to variables (primitive types), is decidable and belongs to the NP class.
A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.
The article examines the approaches of OLAP-applications for business analysis trucking company. Examples of using multi-dimensional tables to support decision-making.
This paper presents further development of distributed multimodal clustering. We introduce a new version of multimodal clustering algorithm for distributed processing in Apache Hadoop on computer clusters. Its implementation allows a user to conduct clustering on data with modality greater than two. We provide time and space complexity of the algorithm and justify its relevance. The algorithm is adapted for MapReduce distributed processing model. The program implemented by means of Apache Hadoop framework is able to perform parallel computing on thousands of nodes.
his book constitutes the proceedings of the 23rd International Symposium on Foundations of Intelligent Systems, ISMIS 2017, held in Warsaw, Poland, in June 2017. The 56 regular and 15 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers include both theoretical and practical aspects of machine learning, data mining methods, deep learning, bioinformatics and health informatics, intelligent information systems, knowledge-based systems, mining temporal, spatial and spatio-temporal data, text and Web mining. In addition, four special sessions were organized; namely, Special Session on Big Data Analytics and Stream Data Mining, Special Session on Granular and Soft Clustering for Data Science, Special Session on Knowledge Discovery with Formal Concept Analysis and Related Formalisms, and Special Session devoted to ISMIS 2017 Data Mining Competition on Trading Based on Recommendations, which was launched as a part of the conference.
An approach to discovering rules in nonstationary k-valued Multidimensional time series is proposed. It allows one to discover rules that are subject to “smooth” structural changes with the course of time. A measure of rule similarity is proposed to describe such changes, and its application in the form of weight in the graph of rules is discussed. The discovered rules can be used to predict the next elements in the multidimensional time series, to analyze the phenomenon described by this multidimensional time series, and to model it. This allows one to use the proposed algorithm for predicting time series and for examining and describing the processes that can be represented by a multidimensional time series. Means for the direct practical application of the proposed methods of the analysis and prediction of time series are described, and the use of those methods for the short-range prediction of a real-life multidimensional time series consisting of the stock prices of companies operating in similar fields is discussed.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables