Open AccessQuality Healthcare Prediction using K Means And Clara Partition Based Clustering Algorithm For Big Data Analytics
Author(s) -
Madhura Chinchmalatpure,
Mahendra P. Dhore
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c4828.029320
Subject(s) - cluster analysis , computer science , big data , data mining , partition (number theory) , analytics , scalability , set (abstract data type) , data set , data science , machine learning , artificial intelligence , database , mathematics , combinatorics , programming language
Big Data is a collection of large or vast amount of information that grows at ever increasing rates. Big data is ordered, unstructured, semi structured or mixed data in natural world. Researchers are designing, implementing, analyzing different application. In medicinal industry large or vast amount of data is available but people are not able to extract the significant information. Healthcare big data analytics (HBDA) becomes “Healthier analytics” by fusion of techniques. In this paper, we discuss and implement algorithms of clustering using R-Studio tool. Clustering is defined as the method of partitioning set of patterns into similar groups called as clusters. We can extract the data from vast datasets in the form of clustering rules. These clustering techniques are scalable. Also, we compare the accuracies of two partition based clustering techniques k-means and Clara on healthcare datasets for giving good quality of healthcare services. Implemented results demonstrate the k-means method gives better accuracy values than Clara algorithm.