K-Means & Ward?s Algorithm with HBO in Spatial Data Mining


Author(s): Sophiya, Saurabh Sharma

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. In this paper, a study based on K-means and Ward’s Algorithm with Honey Bee optimization is done for spatial data mining and finally an algorithm is created for data clustering also. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining. So, by this algorithm clustering can be done in a most appropriate way and can be used for further study.