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Application of Enhanced Clustering For Different Data Mining Techniques
| Content Provider | Semantic Scholar |
|---|---|
| Author | Suganyadevi, P. |
| Copyright Year | 2016 |
| Abstract | Iris data are rather very complex and it is very difficult to predict the behavior of runoff based on temporal data sets. This paper has been proposes a Modified approach K-Means clustering and enhanced pca algorithm which executes K-means algorithm this Algorithm approach is better in the process in large number of clusters and its time of execution is comparisons base on K-Mean,DBSCAN algorithm approach. If the process experimental result is using the proposed algorithm it time of computation can be reduced with a group in runtime constructed data sets are very promising. Modified Approach of K Mean Algorithm and enhanced pca is better than K Mean and dbscan for Large Data Sets. Index Terms – Temporal, Clustering, Data mining, Hierarchical, Hard and soft clustering, Hydrological process, Time series sequences. Dbscan, Mkmeans, Epca. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijeter.everscience.org/Manuscripts/Volume-4/Issue-1/Vol-4-issue-1-M-17.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
