The Clustering Approach for Nutrient Foods

Abstract

Clustering is the process of grouping the data into classes of similar objects. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Our system will implement the clustering of nutrient foods by using the k-mean partitioning method. Each cluster's center is presented by the mean value of the objects in the cluster. The k-means algorithm is by far the most widely used method for discovering clusters in data. This paper presents our system and shows how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by Euclidean distance measurements between each pair of objects. This system focuses on nutrient foods dataset, which contains 253 instances and eleven attributes from UCI machine learning repository.