Abstract
Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithmâ??s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. Itâ??s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets.
Keywords
data clustering, weka , k-means, hierarchical clustering
DOI
View DOI - (https://doi.org/10.36713/epra8308)
How to Cite:
Aastha Gupta, Himanshu Sharma, Anas Akhtar , A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING , Volume 7 , Issue 8, august 2021, EPRA International Journal of Multidisciplinary Research (IJMR) , DOI: https://doi.org/10.36713/epra8308