Data mining extracts and presents data from large sets, often used in business intelligence. Regression, association, factor analysis, and categorization are common models used to analyze data and find relationships and trends.
Data mining describes the process of extracting data from large sets of information and presenting it in a unique way. This process is often found in business intelligence studies, where experts mine large data sets about a market or business operations and attempt to uncover previously unacknowledged relationships and trends. A data mining model refers to the techniques specialists use to group and present information, as well as the ways they can apply the information to certain questions and problems.
Many specialists consider regression data mining to be the most basic and commonly used data mining model. In this process, an expert analyzes a set of data and creates a formula that describes it. Many financial analysts use this technique to forecast prices and market trends. This model works best in scenarios where the data is expected to remain consistent.
Another popular data mining model is based on association. A specialist can analyze the datasets to determine which components often appear together. When two components are paired again and again, a researcher can assume that there is some association between them. For example, a researcher using data mining to understand the performance of a retail store might find that consumers often buy pens and pencils at the same time they buy paper. A manager can use information learned from a data mining model to increase sales by displaying all associated items in one space.
Factor analysis is another common data mining model. In this process, a researcher collects a number of different variables and attempts to pinpoint the factors driving the fluctuations in value. A market researcher, for example, can learn from a customer base how they evaluate the characteristics of similar products. A researcher can then organize this information to illustrate the factors that determine consumers’ evaluation of the characteristics. While proponents of this model believe it can highlight commonality between seemingly disparate variables, some critics believe this model may lead some interpreters to assume the cause of certain phenomena when all the information needed to determine the cause may not be available.
Researchers can use a categorization-based data mining model for simpler problems. Using this technique, specialists organize data according to their classifications and tend to organize them in a visual form, such as a tree or a graph. This type of model is particularly useful in scenarios where an individual must choose between several options in each category. A designer might find this model useful if at each stage of a process she can choose between different materials.
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