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Please explain in detail.
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Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes.
Its application includes using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more in a business.
Quite similar to data analysis but Data mining allows you to:
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Sift through all the chaotic and repetitive noise in your data. -
Understand what is relevant and then make good use of that information to assess likely outcomes. -
Accelerate the pace of making informed decisions.
Few Key data mining techniques include:
Classification analysis
Classification analysis is used to classify distinct data in a different class. It is used to restore significant information related to data and metadata.
Association Rule Learning
Association rule learning refers to the process that enables to identify relations between distinct variables in a large set of data.
Outlier detection
Outlier detection refers to the data observation in a database that does not match an expected pattern.
Clustering Analysis
The term ‘cluster’ is the collection of data objects which are similar within the same cluster.
Regression Analysis
Regression analysis is the process of analyzing and identifying the relationship among the different variables.