Efficient K-Mean Algorithm for Large Dataset

The term data mining is used to discover knowledge from large amount of data. For knowledge discovery many software haven developed, that is known as data mining tools these are statistical, machine learning, And neural networks. K-means and K-medoids are widely used simplest partition based unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters; technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Stored data is used to locate data in predetermined groups called class. Data items are grouped according to logical relationships or consumer preferences called cluster. Data can be mined to identify association. Data is mined to anticipate behavior patterns and trends called sequential patterns

Association Rule Mining on Metrological and Remote Sensing Data With Weka Tool

Drought is one of the major environmental disasters in many parts of the world. There are several possibilities of drought monitoring based on ground measurements, hydrological, climatologically and Remote Sensing data. Drought indices that derived by meteorological data and Remote Sensing data have coarse spatial and temporal resolution. Because of the spatial and temporal variability and multiple impacts of droughts, we need to improve the tools and data available for mapping and monitoring this phenomenon on all scales. In this paper we present discovering knowledge by association rules from metrological and Remote Sensing data and we have also used descriptive modeling. For calculating drought taking metrological data which is extract from metrological department of Pune at Maharastra (India) and Remote Sensing data is extract from National Aeronautics and Space Administration (NASA)