Rainfall flow optimization based K‐Means clustering for medical data

SummaryIn the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set.