Open AccessInfluence of an efficient Hierarchical Clustering Algorithm in analyzing Cancer affected DNA Dataset
Author(s) -
E. Kiruba Nesamalar,
Jeetendra Kumar,
T. Amudha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1998/1/012030
Subject(s) - cluster analysis , hierarchical clustering , computer science , data mining , medoid , correlation clustering , data set , set (abstract data type) , cure data clustering algorithm , consensus clustering , canopy clustering algorithm , single linkage clustering , artificial intelligence , machine learning , pattern recognition (psychology) , programming language
This research work presents an influence of hierarchical clustering approach in anlyzing cancer affected DNA data set. The primary objective of this research work is to identify the best clustering algorithms to group cancer-affected DNA datasets. Data analysis shows an important role in bioinformatics. Data analysis technique used for grouping the data objects is based on unsupervised learning. Clustering is an unsupervised learning technique in data mining. It groups a set of clusters from the entire dataset. In this research work, 700 cancer-affected DNA datasets are considered for analysis. This research work compares three types of Clustering techniques, K-Means (KM), K-Medoids (KMS), and Hierarchical Clustering (HC), to group cancer-affected DNA. Each algorithm has some strengths and weaknesses. These clustering algorithms are compared in detail based on various parameters. Results prove that the hierarchical clustering algorithms show lesser execution time and increased accuracy than other KM and KMS algorithms.