Intelligent Hybrid Cluster-Based Classification Algorithm For Efficient Data Analysis

Abstract

Large amounts of data are produced by a number of industries, including financial services, healthcare, retail, pharmaceutical, telecom and etc. Quick processing of this large quantity of data is necessary to obtain important business insights. A large amount of data must be reacted to in real time, or nearly in real time, in order to meet the new standards. Using similarity to organize data objects into clusters, one important technique for unsupervised data processing is clustering. Multiple fields, including statistics, data mining, pattern recognition, and decision science, have examined and utilized clustering. The two primary categories of clustering algorithms are partitional and hierarchical clustering techniques. By using existing techniques, the huge amount data can’t be handled without effectiveness. To generate a final clustering, a function has been used for the clustering aggregation. First, an extensive amount of basic clustering is produced by this hybrid clustering technique. Even this technique can handle large-scale datasets and extracting valuable conditions from complex data structures. Therefore, for more efficient data analysis, the intelligent hybrid cluster-based classification algorithm performs better in terms of efficiency, accuracy, and precision.