Distributed knowledge data mining device and mining method used for complex network

Abstract

The invention discloses a distributed knowledge data mining device and method used for a complex network. The distributed knowledge data mining device adopts a distributed computing platform which is composed of a control unit, a computing unit and a man-machine interaction unit, wherein the innovation key is to finish the calculated amount needed by a multifarious clustering algorithm in the data mining by different servers so as to improve the efficiency of the data mining. Aiming at different knowledge data, the degrees of relation and the weights of knowledge data also can be computed by applying different standards, so that a more credible result is obtained. A second-level clustering mode is adopted in the knowledge data mining process; the result of the first-level clustering is relatively rough, but the computing complexity is very low; and the computing complexity of the second-level clustering is relatively high, but the result is more precise. By combining the first-level clustering with the second-level clustering efficiently, the distributed knowledge data mining device improves the time complexity and clustering precision greatly in comparison with the traditional first-level clustering mode. According to the invention, as a visual and direct exhibition network structure and a dynamic evolutionary process are adopted, references are provided for the prediction in the fields of disciplinary development and hotspot research.

Description

technical field [0001] The invention relates to a distributed knowledge data mining device and mining method for complex networks, belonging to the computer application technical field of data mining. Background technique [0002] At present, the new data generated by human society every day is growing rapidly in the form of explosion. Real-time analysis and processing of these massive data and mining their internal relationships are issues of great concern to analysis and decision makers. For example, the development speed of my country's information science is astonishing. Thousands of scientific research projects are declared every year, and the number of published papers and patent applications are countless. Analyze the relationship network between the knowledge data of these scientific research projects, papers and patents, and predict Research hotspots or focuses in this technical field in the next few years can help scientific research management departments to more e...

AI Technical Summary

Problems solved by technology

[0013] However, the existing technology has the following disadvantages: (1) massive data processing cannot meet the real-time requirements, (2) most algorithms cannot effectively analyze the information of knowledge data including scientific research project data, paper data and patent data

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