Current Trends in Datamining Techniques

Abstract

Society generates massive amounts of data from various sources such as business, science, medicine, economics, sports, web data, and so on. Databases, data warehouses, and other information repositories house massive amounts of data. The availability of large datasets and the growing importance of data analysis for scientific discovery are spawning a new class of high-end applications. Data mining and scientific data analysis are examples of this type of application. Data mining is the process of gaining knowledge by analysing data stored in very large repositories, which are analysed from various perspectives and the result is summarised into useful information. The process entails analysing historical data and forecasting future occurrences or events based on that analysis. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.