Mining Mechanism Of Knowledge Clusters And Associations Based Cloud Platform

With the improvement of the science and engineering, the Internet is also growing amount of data. The traditional stand-alone approach can no longer meet for the overall operation of the site analysis. Under the background of big data, various text data massively in people’s daily lives. This paper text summary, references, keywords and other multi-dimensional data mining collate recommended text information in order to improve the efficiency and quality of readers. The whole idea of this article is the first of a static text clustering, making the text information is automatically archived. Then based on the user’s browser process to do dynamic association rule analysis, dynamic text data frequent item sets. The final analysis will find frequent item sets clustering results its association rules, in order to improve the efficiency of query text information. It has a very important significance and application prospects.This article refers to the experimental environment Hadoop platform, made on the basis of the existing clustering improvements. It is proposed based on weighted matrix FP-Growth association rules by improving the correlation algorithm of the literature author information mining process and processing time mining hidden information. Indicators of the degree of experiments, the improved algorithm performance and space efficiency will find the document author interrelated and get references the authors’ knowledge expert profiles.