Data mining analysis provides different results based on the algorithm used. Common types include EDA, descriptive modeling, predictive modeling, and pattern and rule discovery. Each tool offers a unique perspective, allowing professionals to gain insight into concerns.
Data mining analysis can be a useful process that yields different results depending on the specific algorithm used to evaluate the data. Common types of data mining analytics include exploratory data analysis (EDA), descriptive modeling, predictive modeling, and pattern and rule discovery. Using each of these data mining tools provides a different perspective on the information collected. Professionals using these techniques can gain additional insight into a concern or issue of concern based on the specific analysis tool used.
Because of the different results that data mining analysis tools provide when used, a basic review of each should be considered. Exploratory data analysis, or EDA, involves reviewing a dataset with no clear outcome goals for review. The variables that define the data are used as a basis for providing visual representations to the researcher. As the number of variables increases, this analysis tool can become less effective at visualizing data.
Descriptive modeling is a data mining analysis tool used to collectively describe all the data in a given dataset. Specifically, this approach synthesizes all the data to provide insight into the trends, segments, and clusters present in the information being searched. Descriptive data mining analytics is commonly used in advertising. An example of this is market segmentation where marketers take larger groups of customers and segment them by similar characteristics.
Other tools also include predictive modeling. Predictive modeling involves developing a model based on existing data. The model is then used as the basis for predicting another variable relevant to the data being examined. The term “predictive” means that this data mining tool can allow the user to predict a value based on what is known in the dataset. Predictive analytics can be used by marketers to determine what products customers are looking for. Based on current purchasing trends, marketers may be able to make predictions about which new products may be popular in the future.
Pattern and rule discovery differs from descriptive and predictive data mining tools. While descriptive and predictive tools use model building as a basis for analysis, pattern and rule discovery focuses on identifying patterns in the data. Marketers who work for grocery stores, for example, often use this data mining analytics tool as a means to determine purchasing patterns. By determining which products customers consistently buy in the same order, you can develop targeted promotions for your items.
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