Approaches to Partition Medical Data using Clustering Algorithms

Abstract

successful application of data mining in fields like e- business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Data is a great asset to meet long-term goals of any organization and can help to improve customer relationship management. It can also benefit healthcare providers like hospitals, clinics and physicians, and patients, for example, by identifying effective treatments and best practices popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Efficient clustering tools reduce demand on costly healthcare resources. It can help physicians cope with the information overload and can assist in future planning for improved services. Clustering results are used to study independence or correlation between diseases and for better insight into medical survey data. To achieve this, create clustering algorithms that enhances the traditional K-Means, DB-Scan and Fuzzy C-Means algorithms.