| Summary: | Clustering as a method of grouping objects into some cluster is very
important in pattern recognition and pattern classification. Over the years, many
methods have been developed for clustering patterns. Fuzzy clustering and hard
clustering are method used to clustering process. Fuzzy C-means is one of many
methods of clustering based on fuzzy approach, while K-Means and K-Medoid
are methods clustering based on crisp approach.
This study is aimed to application that methods (Fuzzy C-Means,
K-Means dan K-Medoid) on stock data clustering of food and beverage company.
The result clustering of methods to find optimal cluster. Dunn’s Index (DI)
method used to find cluster validaty.
Cluster analysis aims at identifying groups of similar objects and,
therefore helps to discover distribution of patterns and interesting correlations in
large data sets. In the marketing area, this activity known as segmentation
(grouping) stock value in the market. Investor can using result clustering of food
and beverage company of decision making investment exactly.
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