Data Mining

Data Mining

Data mining is an umbrella term that can be applied to a number of varying activities. In corporate world, data mining is used to determine the direction of trends and predict the future. It uses large amount of computing power for its operation. Data mining is popular in the fields of mathematics and science but is now increasingly used by marketers also. Data mining is also known as Knowledge-Discovery in Databases(KDD). Parameters of Data mining are: Association, Sequencing, Classification, Clustering and Forecasting. Data mining applications are available on all size systems for mainframe, client/server and PC platforms. Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. Data mining process consist of six steps and are data cleaning, data mart, derived attributes, modeling, post-processing and deployment. Data miners uses some data mining techniques like near-neighbor models, k-means clustering, decision tree and/or k-fold cross validation etc. There are four basic model of data mining and are Predictive Model, Summary Model, Network Model and Association model. General mining software used for mining are Mil Shield, ADAPA Predictive Analytics, Younicycle, STATISTICA Data Miner, VisuMap and many more.
For detail information regarding parameters, process, techniques and models of data mining mentioned above please refer the following files. It includes information about application areas of data mining as well:

Data Mining.pdf

Data Mining presentation.ppt

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for more information refer the links as given below:

http://rtmnupervasivecomp.blogspot.com
http://rtmnuittrends.blogspot.com
http://www.rtmnunetworkingtechnology.blogspot.com

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