Implementation and Evaluation of Diabetes Management System Using Clustering Technique

Data mining is a field of computer science which is used to discover new patterns for large data sets. Clustering is the task of discovering groups and structures in the data that are in some way or another similar without using known structures of data. Most of this data is temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. The paper proposes a data analysis and visualization technique for representing trends in temporal data using a clustering based approach by using a system that implements the cluster graph construct, which maps data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, a clustering-based technique is used, to visualize temporal data to identifying trends for controlling diabetes mellitus. Given the complexity of chronic disease prevention, diabetes risk prevention and assessment may be critical area for improving clinical decision support. Information visualization utilizes high processing capabilities of the human visual system to reveal patterns in data that are not so clear in non-visual data analysis