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...
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| Vydané v: | International journal of advanced computer research Ročník 4; číslo 3; s. 799 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Bhopal
Accent Social and Welfare Society
01.09.2014
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| Predmet: | |
| ISSN: | 2249-7277, 2277-7970 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. |
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| Bibliografia: | 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 |