Improving Efficiency of Similarity of Document Network Using Bisect K-Means

In today's internet world, most of the document related to various fields are stored in digital or electronic format. It allows easy access to users and also save the space. So the main challenge is to retrieve the interested document from huge set of document dataset. For this problem, document clustering becomes popular solution in the area of data mining. In this approach, all similar kind of document are stored collectively. This paper focus on the documents of clinical cases. The proposed system makes cluster of similar kind of clinical cases documents and improves the search efficiency. For document clustering two kinds of techniques are used named as K-means and topic detection. Initially, Document network is formed by linking various kinds of clinical reports. On this network, Bisect K-means clustering algorithm is applied after cluster formation, system identify the unique topic for each cluster. The performance of the system is tested on Vaccine Adverse Event Reporting System (VAERS) dataset and evaluation results prove that Bisect K-means is more accurately divide the number of clinical reports in appropriate cluster, than K-means clustering algorithm. The performance of the system is rely on three terms: precision, recall, and F-measure.