Designing a Knowledge Discovery of Clustering Techniques in Pharmaceutical Compounds - Palanisamy, V; Kumarkombaiya, A (International Journal of Computer Sciences and Engineering, 2015)
To develop data mining techniques to support decision making and discovery of functional group of the connectivity atom for drug effects by analyzing chemical compound data in the form of structured data. Existing studies in data mining mostly focus on hierarchical clustering techniques applied in large and small dataset of pharmaceutical compound and analyse its performance based on time accuracy. In this paper focuses to apply cluster techniques of partition method like Enhanced K-means algorithm and hierarchical method like Birch and Chameleon algorithm used in pharmaceutical compound specifically represented as atom number, atom name like carbon, hydrogen, nitrogen, oxygen with connected atoms. These dataset form a functional group of atoms by functioning in three phases. The performance can be experimented based on time taken to form the estimated cluster, also overall execution time can be reduced by improvement of Enhanced Kmeans algorithm when compared to chameleon and Birch algorithm.