SPSS Data Mining by IBM

SPSS data mining is a specific statistical analysis computer program that is used to mine for data in a large "data warehouse" or database. This data can be anything from numbers, weights, ages, names, gender, geographical location, political affiliation, food preferences etc. There is no limit to the type of data that can be collected. Once the data has been collected into a database data mining essentially is the process of discovering relationships, correlations and patterns between the collected amounts of specific data. SPSS software is one of many programs available to help with the process of data mining.

Who Uses SPSS Data Mining?

Since the SPSS data mining software is specifically geared toward analyzing social data SPSS data mining is the most popular way to mine for data when analyzing or predicting social behaviors, tendencies and attitudes. Which means that is most often used by people or companies who want to analyze data about people and their behaviors. This includes market researchers, government agencies, survey companies, retailers, internet companies, political think tanks, education researchers and anyone else interested in predicting social trends.

Why is SPSS Data Mining so Popular?

There are many reasons why SPSS Data mining is popular. One reason is because it is one of the oldest methods of statistical analysis. It was first developed in 1968 by Norman H. Nie and C. Hadlai Hull and has only gotten better since. Another reason for its popularity is its ease of use. Most of the features in SPSS can be accessed by familiar pull-down style menus. However, another attractive feature of SPSS is its flexibility and the ability to write your own syntax commands. It is also supports full integration with Windows, Mac OS and Linux run applications and supports multiple interface languages so it can be used easily by international teams. SPSS data mining also offers its users multithreaded algorithms, nonparametric testing procedures and a wide range of regularization methods including, regression (both Ridge and Cox), Lasso and Elastic Net, and various classification analysis. Essentially it's popular because it is powerful, innovative, flexible and easy to use.

Sources:

IBM, IBM SPSS Statistics Products, www.spss.com