Application of K-Means and Partitioning Around Mediods (PAM) clustering techniques on Maize and Beans yield in Tanzania

Simple item page
dc.contributor.authorMbukwa, Justine Nkundwe
dc.contributor.authorAnjaneyulu, G.V.S.R.
dc.date.accessioned2024-04-05T05:28:49Z
dc.date.available2024-04-05T05:28:49Z
dc.date.issued2016
dc.descriptionArticle published by KY Publications in the Bulletin Of Mathematics And Statistics Research Vol.4.Issue.4. p. 146-158
dc.description.abstractThis paper contributes to the application of k-means and k-mediods multivariate statistical methods for the purpose of revealing optimal clusters and assessing the consistency of individual districts within the group. Data used were extracted from united republic of Tanzania (Ministry of Agriculture, Livestock and Fisheries(MALF) (2003/12) consisted a total of (n=36 districts) with both maize and beans yield. The R-statistical computing version (3.1.1) was used. The study findings revealed that 36 districts were grouped into six clusters using k-means algorithm. Using the k-mediods, it was revealed that only 11 districts were found to very well structured since their silhouette width (Si) is above 0.5. Nevertheless, the clusters validation was done in such a way that individual district whose silhouette width (Si) close to 1 was regarded as highly consistency clustered whereas districts with (Si) greater or equal to 0.25 were said to be somehow well clustered and otherwise. The paper concludes that few districts that are very consistent given the threshold margin should to be monitored and evaluated effectively to ascertain productivity. The study recommends that the government should pay attention on allocating the scarce resources to the consistency clusters along with policy review in favour of smallholder farmers through access and timely for all important farm inputs in future.
dc.description.sponsorshipPrivate
dc.identifier.citationAPA
dc.identifier.issn2348-0580
dc.identifier.issnhttp://bomsr.com/4.4.16/146-158%20JUSTINE%20NKUNDWE%20MBUKWA.pdf
dc.identifier.urihttps://scholar.mzumbe.ac.tz/handle/123456789/552
dc.language.isoen
dc.publisherKY Publications
dc.subjectClustering
dc.subjectk-means and PAM or k-mediods
dc.subjectk-methods
dc.titleApplication of K-Means and Partitioning Around Mediods (PAM) clustering techniques on Maize and Beans yield in Tanzania
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Mbukwa, J. & Anjaneyulu, G._FST_2016.pdf
Size:
623.56 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Research Articles