PENERAPAN ALGORITMA INVASIVE WEED OPTIMIZATION UNTUK PENENTUAN TITIK PUSAT KLASTER PADA K-MEANS
K-means is one of the most popular clustering algorithm. One reason for the popularity of K-means is it is easy and simple when implemented. However, the results of K-means is very sensitive to the selection of initial centroid. The results are often better after several experi...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/130631/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=71057 |
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| Institution: | Universitas Gadjah Mada |
| Summary: | K-means is one of the most popular clustering algorithm. One reason for
the popularity of K-means is it is easy and simple when implemented. However,
the results of K-means is very sensitive to the selection of initial centroid. The
results are often better after several experiment. Another reason why K-means
stuck in local optima is due to the method of determining the new center point for
each iteration that is performed using the mean value of the data that exist on the
cluster. This causes the algorithm will do search for the centroid candidates
around the center point. To overcome this, implement a method that is able to do a
global search to determine the center point on K-means may be able to assist Kmeans in finding better cluster center. Invasive Weed Optimization (IWO) is a
global search algorithm inspired by weed colonization process. In this study
proposed a method which is the result of hybridization of K-means and IWO
(IWOKM). Performance of the method has been tested on flower Iris dataset. The
results are then compared with the result from K-means. The result show that
IWOKM able to produce better cluster center than K-means. |
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