An association rules text mining algorithm fusion with K-Means improvement

With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big limitations that the K-Means algorithm has, namely requirements that users give the anticipated variety beforehand integer K and random selection of initial variety center, proposed K-Means improved algorithm based on the association rules technology. This method proposed the concept of the smallest rule covering set .It has relieved these two big limitations of K-Means algorithm effectively. It is used for the audit monitor target discovery and extraction process in social security domain basic old-age insurance audit methods. Thus it can provide better reference value and guiding sense for auditors.