COMPARITIVE ANALYSIS OF MACHINE LEARNING ALGORITHM
| Author(s) | : | Shweta Verma, Yamini Chouhan |
| Institution | : | Research Scholar, Department of Computer Science and Engineering, Shri Shankaracharya Group of Institutions, Junwani, Bhilai (C.G.), India |
| Published In | : | Vol. 5, Issue 6 — June 2018 |
| Page No. | : | 224-230 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Clustering problem is an unsupervised learning algorithm. It is a method that partition information objectsinto matching clusters. The records items inside the identical cluster are quite much like each different and multipleinside the different clusters. Clustering is an unsupervised learning algorithm of hassle that is used to determine theintrinsic grouping in a set of unlabeled statistics. Grouping of gadgets is completed on the principle of maximizing theintra-class similarity and minimizing the inter-class similarity in this kind of way that the items within the samegroup/cluster share a few similar homes/traits. There is a huge range of algorithms to be had for clustering. This paperprovides a comparative analysis of diverse clustering algorithms. In experiments, the effectiveness of algorithms isevaluated through comparing the effects on 4 datasets. Our main aim to show the comparison of the different- differentclustering algorithms of WEKA and find out which algorithm will be most suitable for the users.
Shweta Verma, Yamini Chouhan, “COMPARITIVE ANALYSIS OF MACHINE LEARNING ALGORITHM”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 6, pp. 224-230, June 2018.
