What is the data mining process?
The data mining process is a tool for detecting statistically significant formulas in large amounts of data. It usually includes five main steps that include preparation, data exploration, model building, deployment and control. Each step in the process includes a different set of techniques, but most use some form of statistical analysis.
Before starting the data mining process, scientists usually set research objectives. This step of preparation usually determines what types of data it is necessary to study, what data mining techniques should be used and what form the results will have. This initial step in the process can be essential for collecting useful information.
The next step in the data mining process is a survey. This step usually includes the assembly of the required data from the information warehouse or the collection entity. Then logging experts usually prepare data sets for analysis. This step usually lies in the collection, cleaning, organization of Akontrol of all data for errors.
These prepared data usually enterto the third step in the data mining process, building model. To achieve this, scientists usually take small data samples and apply different data mining techniques to them. The modeling step is often used to determine the best method of statistical analysis needed to achieve the desired results.
Four main techniques can be used in the data mining process. The first is a classification that organizes data in predefined groups or categories. In the second technique, called Clustering, scientists allow computer to organize data into groups as selected. The third data mining technique is looking for connections between variables. The fourth usually looks for sequential formulas in data that can be used to predict future trends.
The final step in chasing Datcess is deployment. For this purpose, the techniques selected in the model are applied to a larger data file and the results are analyzed. A message that comes from thisStep, usually shows patterns found throughout the process, including any classifications, clusters, associations or sequence formulas existing in the data file.
Review is often an important last step. This phase usually includes repetition of mining models with a new data set to ensure that the main set was representative of the entire data population. The results cannot predict trends in a larger population unless the data sample is exactly.