Proceedings of the 11th IEEE International Conference “Application of Information and Communication Technologies” (AICT-2017)
A novel approach to triclustering of a three-way binary data is proposed. Tricluster is defined in terms of Triadic Formal Concept Analysis as a dense triset of a binary relation Y , describing relationship between objects, attributes and conditions. This definition is a relaxation of a triconcept notion and makes it possible to find all triclusters and triconcepts contained in triclusters of large datasets. This approach generalizes the similar study of concept-based biclustering.
The article describes formation and realization of the Japanese state scientific policy as the national program of development of the Japanese society. This process is considered throughout 30 years - 1970 to 2000, with a periodization of stages - decades. The analysis of the given experience has concrete practical value for our country which at all levels of the power has proclaimed the direction on development of a scientific and information society. This process is of main value from the point of view of planning, strategy, and also practice and technologies of realization.
At the present level of development the information and knowledge become important engines of global economic growth and key elements of national strate-gy for increasing country’s competitiveness in the international market. The article is aimed to analyze two monitoring systems of innovation capacity (ICT Development Index and Networked Readiness Index) as the indicators of development of knowledge economy and information society.
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.
In this paper some of the task assignment methods and approaches are examined. The analysis of the algorithms considered is showing their strengths and weaknesses. Also, the ways of further research are presented, which aims to develop a methodology for task assignment in project management area.