Data Mining Analysis

Data mining analysis is the process of using data mining software and practices to analyze understand patterns in data.  The process of data mining analysis starts with taking data from the source systems and storing it in a means that is more readily available for data mining analysis.  In larger deployments the data mining analysis is always performed on what are known as "Data Warehouses"; but a data mining analysis can also be performed on something as simple and straight-forward as an excel spreadsheet.

Two Types of Data Components

Data mining analysis is a major component that will ultimately influence how useable and valuable the acquired information turns out to be. Once the data has been extracted, stored in an accessible database system, and provided to the IT professional, consultant or analyst, it is time to begin data mining analysis. Just like there are different types of data mining software available, there are varying levels of data mining analysis available as well. Some frequently used data mining analysis methods are artificial neural networks, genetic algorithms, decision trees, nearest neighborhood method, rule induction and data visualization.

Two Types of Data Mining Analysis Methods

Artificial neural networks are frequently used in data mining analysis.  This form of data mining analysis involves a system which uses non-linear predictive models that are enabled through training and quite similar in basic composition to biological neural networks. Another type of data mining analysis method is genetic algorithms, which are based on making the data work more efficiently as a whole or take fewer resources to work in the same capacity. These types of processes mimic those of natural evolution, including genetic combination, mutation, and natural selection.

Additional Data Mining Analysis Methods

Data mining analysis using decision trees is as the name suggests, structures that represent sets of decisions (because of the structure’s tree shape, they are called decision trees.) Because this type of data mining analysis involves decisions, each decision comes with its own set of rules for the data classification. There are specific types of decision tree methods used in data mining analysis, including CART (Classification and Regression Trees) which uses 2-way splits to fracture a set of data, and CHAID (Chi Square Automatic Interaction Detection) which uses chi square tests to create more than two splits. If using CART methods in a data mining analysis, the time spent on data preparation will be slightly less than with CHAID. If the nearest neighbor method (also called the k-nearest neighbor technique) is being used in the data mining analysis, each set of data is classified based on a combination of the classes of the k record(s) it is most similar to in previous sets of data. The rule induction method of data mining analysis develops “if-then” rules to compare against the archives of data, extracting only the useful information that passes the test. The last type of data mining analysis is the method of data visualization, which is based on visual interpretation of existing relationships, such as in charts, graphs or other illustrated graphic tools.

After Data Mining Analysis

Once the data mining analysis has taken place, the end information is placed in a user-friendly format that can easily be interpreted by other users. Usually these formats are placed into graphics or tables.   Through data mining analysis a business can glean greater insights into key business metrics and drivers.  It is this ability of data mining analysis that has made it a high-growth area for enterprise companies.  Data mining analysis, however, is also available for the smaller business.  Companies like EMANIO are making data mining analysis software available at price points and ease of use levels that have previously been unknown.