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Data mining is a process that discovers significant patterns in large amounts of data. It involves five steps: preparation, exploration, model building, deployment, and review. Techniques used include statistical analysis, classification, clustering, association, and sequential pattern analysis. The final step involves applying the techniques to the larger data set and analyzing the results. Review is necessary to ensure the accuracy of the data sample.
The data mining process is a tool for discovering statistically significant patterns in a large amount of data. It typically has five major steps, which include preparation, data exploration, model building, deployment, and review. Each step of the process involves a different set of techniques, but most use some form of statistical analysis.
Before the data mining process can begin, researchers typically set research goals. This preparation phase usually determines what types of data should be studied, what data mining techniques should be used, and what form the results will take. This initial stage of the process can be crucial for gathering useful information.
The next step in the data mining process is exploration. This step typically involves gathering the required data from an information warehouse or collection entity. Then, mining experts typically prepare the raw datasets for analysis. This step usually consists of collecting, cleaning, organizing and checking all data for errors.
This prepared data usually then goes into the third stage of the data mining process, model building. To do this, researchers typically take small samples of data and apply a variety of data mining techniques to them. The modeling stage is often used to determine the best method of statistical analysis needed to achieve the desired results.
There are four main techniques that can be applied in the data mining process. The first is classification, which organizes data into predefined groups or categories. In the second technique, called clustering, researchers let the computer organize the data into groups, however it likes. A third data mining technique looks for associations between variables. The fourth typically looks for sequential patterns in the data that can be used to predict future trends.
The final step in the data mining process is distribution. To do this, the techniques chosen in the model are applied to the larger data set and the results are analyzed. The report that results from this step usually shows the patterns found throughout the process, including any classifications, clusters, associations, or sequential patterns that exist within the dataset.
Review is often an important final step. This stage of the process usually involves iterating mining models with a new data set to ensure that the master set was representative of the entire data population. The results cannot predict trends in the larger population unless the data sample accurately represents it.
