COMPARATIVE STUDY OF VARIOUS TECHNIQUES IN DATA MINING

Data mining (knowledge discovery from data) may be viewed as the extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns and models from observed data or a method used for analytical process designed to explore data. We know Data mining as knowledge discovery. Basically Extraction or “MINING” means knowledge from large amount of data. We use Data mining due to the explosive growth of data i.e. from terabytes to petabytes. We are drowning in data, but starving for knowledge! Alternative names of Data mining are: Data archeology, Data dredging, Information harvesting, Business intelligence, etc. Data mining techniques are used to find the hidden or new patterns to store the data. We know that data mining can use every sector like business, agriculture, marketing etc. There are many techniques for data mining like clustering, classification etc. There are various approaches and techniques of data mining which can be applied on data to build up a new environment to improve performance of existing data and help to create the new predictions on the data. [1].