Improve K-Means Algorithm

The algorithm classifies objects to a predefined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each other. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This research focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This research uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid.

Using milk retantate that resulting from Ultrafiltration technology in making Mozzarella cheese

This study aimed to manifactor mozzarella cheese using bovine milk retentate and concentrated with Ultrafiltration using (Frames & plates) system, and compared it with mozzarella cheese made from original milk in terms of chemical composition, cheese yield, sensory and rheological characteristics.

A New Algorithm for Data Clustering and Enhancing K-Means Algorithm

This paper introduces a new algorithm to solve some problems that data clustering algorithms such as K-Means suffer from. This new algorithm by itself is able to cluster data without the need of other clustering algorithms.

Using milk retantate that resulting from Ultrafiltration technology in making White cheese

This study aimed to manifactor white cheese using bovine milk retentate and concentrated with Ultrafiltration using (Frames & plates) system, and compared it with white cheese made from original milk in terms of chemical composition, cheese yield, sensory characteristics.