A novel Hybrid technique for solving Data Aggregation problem based on Extended Weighted K-means algorithm and Key Performance Indicators

Data clustering is a common method for analyzing data in a wide range of applications, which includes data mining, picture analysis, pattern recognition, and so on. Each feature has a distinct importance in a non-supervised data collection. The clustering outcome will be improved if the feature is set with a correct weight that completely incorporates the lever of influence on the cluster effect. A feature evaluate function is introduced to produce a collection of vectors based on featured weights by reducing the function, which is a multiple objective issue. To solve the issue and determine the feature’s weight, a quick and elitist multi-objective clustering algorithm is applied. This paper proposes an Extended weight clustering technique based on the key performance indicator. The goal is to determine the patient’s chances of heart attack and prioritize the data without redundancy and to evaluate them in the weighted environment and to analyze the performance of the same with the help of key performance indicators.