(untitled) (International Journal of Engineering and Computer Science, 2018)
Staff allotment is a crucial job in industry sector. Human resource plays leading rule to achieve success of the industry. The Data mining is analytical method to allot the right job for right man. This is not easiest job to match the work schedule in large industrial sector. There is a large databases have to be maintain each and every department. There must be a need for perfect knowledge discovery model to retrieve the information about the staff. From that the management can make perfect schedule. The data mining approaches will make suitable to make the work schedule. how can be Data mining techniques and k mean algorithm process the effective job will be the theme of this article. Data mining may be regarded as an evolving approach to data analysis in very large databases that could become a useful tool to management professionals. Data mining involves extracting knowledge based on patterns of data in very large databases. Yet, data mining goes beyond simply performing data analysis on large data sets. Organizations that employ thousands of employees and track a multitude of employment-related information might find valuable information patterns contained within their databases to provide insights in such areas as employee retention and compensation planning. To develop the staff planning and allotment, the k mean clustering algorithm can be used for this job. K mean is a method is popularly for cluster analysis in data mining .k means clustering aims to part ion n observations into k clusters in which each observation belongs to the cluster with nearest mean serving as prototype of cluster. k mean algorithm is can be grouped employees as a different cluster with nearest mean.