Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept
Algorithm 1
To Find the clusters by simple K-Mean clustering algorithm.
Input: Array {a1, a2, a3, …, an}
a = data points
k = Number of Required Clusters
Output: A set of Clusters
Steps:
(1)
Randomly select k data points from dataset D as initial centers.
(2)
Calculate the distance between each data point di (1 < I ≤ n) and all the k clusters Cj (1 ≤ j ≤ k) and recalculate the cluster center by taking the Arithmetic Mean of each cluster.
