DEVELOPMENT OF SEMANTIC FILTering METHODS BASED ON SOLVING THE TASK OF CLUSTERING BY BIOINSPIRED ALGORITHMS
This article is devoted to solving the problems of theoretical knowledge management foundation development for the creation of multi-disciplinary intellectual information systems, semantic search, promoting the development of science promising areas and information technologies related to solving the problems of knowledge integration from heterogeneous distributed sources, the identification of the subject area and conceptual modeling, analysis and retrieval data. The urgency of solving this problem is to expand the application areas of application developed intelligent information systems for a range of class NP- complex urgent tasks. The proposed article considers the clustering problem. In order to improve the efficiency and quality of knowledge management solutions offered are the modified tasks clustering algorithms based on using bioinspired approaches. The developed modifications of the algorithms improve the quality of the solutions obtained. These studies in the field of semantic search constitute an important scientific direction within the framework of the problem of the knowledge management technologies development. The solution of the problem of clustering by means of using of bioinspired algorithms is considered herein. The following two new clustering algorithms: a combined algorithm based on the Artificial Bee Colony algorithm and the k-means algorithm, as well as the Ant clustering algorithm, are proposed. To study the algorithms obtained, their effectiveness and the results quality, software modules that simulate the proposed algorithms have been developed. The analysis of the obtained results, comparison with the classical algorithm, as well as conclusions about the effectiveness of new algorithms. are presented.