Data mining techniques. Brown, M. 2012.
Data mining techniques [pdf]Website  abstract   bibtex   
Many different data mining, query model, processing model, and data collection techniques are available. Which one do you use to mine your data, and which one can you use in combination with your existing software and infrastructure? Examine different data mining and analytics techniques and solutions, and learn how to build them using existing software and installations. Explore the different data mining tools that are available, and learn how to determine whether the size and complexity of your information might result in processing and storage complexities, and what to do. Data mining as a process Fundamentally, data mining is about processing data and identifying patterns and trends in that information so that you can decide or judge. Data mining principles have been around for many years, but, with the advent of big data, it is even more prevalent. Big data caused an explosion in the use of more extensive data mining techniques, partially because the size of the information is much larger and because the information tends to be more varied and extensive in its very nature and content. With large data sets, it is no longer enough to get relatively simple and straightforward statistics out of the system. With 30 or 40 million records of detailed customer information, knowing that two million of them live in one location is not enough. You want to know whether those two million are a particular age group and their average earnings so that you can target your customer needs better. These business-driven needs changed simple data retrieval and statistics into more complex data mining. The business problem drives an examination of the data that helps to build a model to describe the information that ultimately leads to the creation of the resulting report. Figure 1 outlines the process.

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