Comparative Analysis of Clustering Algorithm for Wind Power

Abstract

Data mining is the process of automatically finding useful information in large data repositories. In the field of software data analysis is considered as a very useful and important tool as the task of processing large volume of data is rather tough and it has accelerated the interest of application of such analysis. The purpose of deploying data mining techniques is discovering important patterns from datasets and also provides capabilities to predict the outcome of a future observation. The process of clustering the requirements allows reducing the cost of software development and maintenance. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. In this paper, we can implement two clustering algorithms such as centroid based K-Means clustering and representative object based Fuzzy C means clustering are compared to real time wind datasets. These algorithms are evaluated in terms of time, accuracy, error rate and random index values. FCM can produce efficient results than K-means algorithm.