A Novel Approach to Mathematical Concepts in Data Mining

Source abstract

This paper describes three different fundamental mathematical programming approaches that are relevant to data mining. They are: Feature Selection, Clustering and Robust Representation. This paper comprises of two clustering algorithms such as K-mean algorithm and K-median algorithms. Clustering is illustrated by the unsupervised learning of patterns and clusters that may exist in a given databases and useful tool for Knowledge Discovery in Database (KDD). The results of k-median algorithm are used to collecting the blood cancer patient from a medical database. K-mean clustering is a data mining/machine learning algorithm used to cluster observations into groups of related observations without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques and it is commonly used in medical imaging, biometrics and related fields.