A Robust and Integrated to Detect Online Sales Customer Loyalties Using Novel Constraint Based K-Means Clustering Algorithm Comparing with General Sequential Pattern Method

Abstract

Aim: The aim is to improve and develop the loyalty of the customer identification using constraint based k-means clustering algorithm. Materials and Methods: K-Means clustering algorithm are compared with general sequential pattern algorithm are used to classify robust integrated detection. To achieve maximum accuracy with a K-Means sample size =10 and general sequential pattern sample size=10 was iterated 20 times for accurate outcome. Result and discussion: In this performance of score model validated test accuracy with 85% confidence detecting of online sales customer loyalties by k-mean sequential algorithm, which has accuracy 76% and a G power of 80% and threshold 0.05%, CI 95% mean, and standard deviation. Conclusion: The proposed algorithm K-means has high accuracy than general sequence algorithm for the selected datasets.