Evaluation of Modified K-Means Clustering Algorithm in Crop Prediction
An agricultural sector is in need for well-organized system to predict and improve the crop over the world. The complexity of predicting the best crops is high due to unavailability of proper knowledge discovery in crop knowledge-based which affects the quality of prediction. In data mining, cluster...
Saved in:
| Published in: | International journal of advanced computer research Vol. 4; no. 3; p. 799 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Bhopal
Accent Social and Welfare Society
01.09.2014
|
| Subjects: | |
| ISSN: | 2249-7277, 2277-7970 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | An agricultural sector is in need for well-organized system to predict and improve the crop over the world. The complexity of predicting the best crops is high due to unavailability of proper knowledge discovery in crop knowledge-based which affects the quality of prediction. In data mining, clustering is a crucial step in mining useful information. The clustering techniques such as k-Means, Expectation Maximization, Hierarchical Micro Clustering, Constrained k-Means, SWK k-Means, k-Means++, improved rough k-Means which make this task complicated due to problems like random selection of initial cluster center and decision of number of clusters. This works demonstrates an evaluation of modified k-Means clustering algorithm in crop prediction. The results and evaluation show comparison of modified k-Means over k-Means and k-Means++ clustering algorithm and modified k-Means has achieved the maximum number of high quality clusters, correct prediction of crop and maximum accuracy count. |
|---|---|
| Bibliography: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2249-7277 2277-7970 |