What are the different types of data mining analysis?

Data mining analysis can be a useful process that provides different results depending on the specific algorithm used for data evaluation. Common types of data mining analysis include: Exploration (EDA) analysis, descriptive modeling, predictive modeling and discovery of patterns and rules. The use of each of these data mining tools provides a different view of the information collected. Experts using these techniques can gain additional insight into the problem or problem of concerns based on the analysis tool used.

Due to the different results that data analysis tools provide when used, it is advisable to consider a basic overview of each of them. Data analysis, or EDA, includes an overview of the data file without any clear results for research. The variables that define data are used as the basis for providing visual representations to the researcher. As the number of variables increases, this tool for analysis of Datstan mining can be less efficientfor data visualization.

Descriptive modeling is a tool for analysis of data mining used to describe all data in a given data file. Specifically, this approach synthesizes all data to provide information about trends, segments and clusters that are present in the information they are. Descriptive data mining analysis is commonly used in advertising. One example is the market segmentation in which traders take larger groups of customers and segment them according to homogeneous characteristics.

Data mining tools also include predictive modeling. Predictive modeling includes the development of a model based on existing data. The model is then used as a basis for prediction of another variable that is relevant to review data. The term 'predictive' suggests that this data mining tool can allow the user to predict a certain value based on the DATOa vém file. Traders can use predictive analysis to determine what products customers are looking for. Based on current shopping trends, traders can predict which new products can be popular in the future.

Discovering formulas and rules differ from descriptive and predictive data for data mining. While descriptive and predictive tools use building the model as the basis for analysis, discovering formulas and rules focuses on identifying formulas in data. For example, merchants working for food stores often use this data mining analysis tool as a means to determine purchase formulas. Determining what products customers are constantly buying in the same order can develop targeted promotions for items.