Improving the Performance of Multidimensional Clinical Data for OLAP using an Optimized Data Clustering approach

Anjana Yadav Et.Al

doi:10.17762/turcomat.v12i3.1575

Abstract

Medicine is a fresh way to utilize for curing, analyzing and detecting the diseases through data clustering with OLAP (Online Analytical Processing). The large amount of multidimensional clinical data is reduced the efficiency of OLAP query processing by enhancing the query accessing time. Hence, the performance of OLAP model is improved by using data clustering in which huge data is divided into several groups (clusters) with cluster heads to achieve fast query processing in least time. In this paper, a Dragon Fly Optimization based Clustering (DFOC) approach is proposed to enhance the efficiency of data clustering by generating optimal clusters from multidimensional clinical data for OLAP. The results are evaluated on MATLAB 2019a tool and shown the better performance of DFOC against other clustering methods ACO, GA and K-Means in terms of intra-cluster distance, purity index, F-measure, and standard deviation

Highlights

The huge amount of data is collected in the form of data warehouse [1, 2, 3] to combine all the information about organisations
The decision support system is developed with data clustering for fast accessing the huge data [11, 12] with maximum accuracy of information with respect to future aspects [13, 14]
In Dragon Fly Optimization based Clustering (DFOC), the Dragon Fly Optimization (DFO) is applied on multidimensional clinical datasets to obtain optimal clusters with cluster heads with minimizing the intra-cluster distances among data elements

Summary

Introduction

The huge amount of data is collected in the form of data warehouse [1, 2, 3] to combine all the information about organisations This data information is very difficult to access in minimum time due the big data for OLAP. To improve the performance of OLAP, the data is organised in several groups to save the accessing time and query processing cost. This organisation of data into groups is known as data clustering. Here we utilized the optimization for data clustering on huge multidimensional data sets to obtain optimal results by removing the limitation of K-Means. We implemented a DOFC (Dragon Fly Optimization based Clustering) approach on clinical multidimensional datasets to generate optimal clusters with cluster centroids and compared the results with ACO, GA and K-Means in terms of several parameters

DFOC approach

Purity Index

RESULTS

Conclusion