ONTOLOGY GENERATION FOR SEMANTIC SEARCH SYSTEM USING DATA MINING
SEMANTIC SEARCH SYSTEM
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Data Mining Techniques comprise Tracking patterns, Classification, Association, Outlier detection, Clustering, Regression and Prediction. First step of Data mining is tracking patterns in which data mining is learning to recognize patterns in data sets. Classification is the second step in data mining which is a more complex data mining technique that forces users to collect various attributes. Association is related to tracking patterns, but is more specific to dependently linked variables. Outlier detection simply recognizing the overarching pattern can't give a clear understanding of the da...
Data Mining Techniques comprise Tracking patterns, Classification, Association, Outlier detection, Clustering, Regression and Prediction. First step of Data mining is tracking patterns in which data mining is learning to recognize patterns in data sets. Classification is the second step in data mining which is a more complex data mining technique that forces users to collect various attributes. Association is related to tracking patterns, but is more specific to dependently linked variables. Outlier detection simply recognizing the overarching pattern can't give a clear understanding of the data set which is regarded as the third step in data mining. Next Step in data mining in Clustering is very similar to classification, but involves grouping chunks of data together based on their similarities. Regression, used primarily as a form of planning and modeling, is used to identify a certain variable, given the presence of other variables. Final step of the data mining technique is Prediction. It is one of the most valuable data mining techniques.
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