Statistical data mining is a computer-based method of analyzing information to discover patterns and correlations. It has practical applications in medicine, business, and design. Data mining involves five main steps, including collecting and organizing data, creating a management system, accessing the data, analyzing it using software, and interpreting the results. The process integrates analytical and transactional data systems using open-ended user questions to sort the data. Statistical data mining collects three types of data: operational, non-operational, and metadata. It has practical applications in fields such as biosurveillance and computer programming.
Statistical data mining, also known as knowledge or data discovery, is a computer-based method of gathering and analyzing information. The data mining tool takes data and classifies information to discover patterns or correlations that can be used in important applications, such as medicine, computer programming, business promotion, and robotic design. Statistical data mining techniques use complex mathematics and complex statistical processes to create an analysis.
Data mining has five main steps. The first data mining application collects statistical data and puts the information into a warehouse-like program. After that, the data in the warehouse is organized and a management system is created. The next step creates a way to access the managed data. Then, the fourth step develops software to analyze the data, also known as data mining regression, while the final step facilitates the use or interpretation of the statistical data in a practical way.
In general, data mining techniques integrate analytical and transactional data systems. Analytical software sorts both types of data systems using open-ended user questions. Open-ended questions allow for countless answers so that programmers don’t influence the sorting results. Programmers create lists of questions to help categorize information using a general focus.
Sorting then relies on the development of data classes and clusters, associations found in the data, and attempts to define patterns and trends based on the associations. For example, Google collects information about users’ shopping habits to facilitate the placement of online advertising. The open-ended questions used to sort through this buyer data focus on the shopping preferences or viewing habits of Internet users.
Computer scientists and programmers focus on analyzing the collected statistical data. Decision tree building, artificial neural networks, nearest neighbor method, rule induction, data visualization, and genetic algorithms all use statistically mined data. These classification systems help interpret the associations discovered by analytic data programs. Statistical data mining involves small projects that can be done on a small scale on a home computer, but most data mining association sets are so large and data mining regression so complicated that they require a supercomputer or network of high speed computers.
Statistical data mining collects three general types of data, including operational data, non-operational data, and metadata. In a clothing store, operational data is basic data used to run the business, such as accounting, sales, and inventory control. Non-operating data, which is indirectly related to the business, includes estimates of future sales and general information about the domestic apparel market. Metadata is about the data itself. A program that uses metadata could sort the store’s customers into classifications based on the gender or geographic location of the clothing shoppers, or the customers’ favorite color, if such data was collected.
A data mining application can be extremely sophisticated, and the statistical data mining tool can have widespread practical applications. The study of epidemics is an example of this. A 2000 data mining project analyzed the cryptosporidium outbreak in Ontario, Canada, to determine the causes of the increase in disease cases. The data mining results helped link the bacteria outbreak to local water conditions and a lack of adequate municipal water treatment. A field called “biosurveillance” uses the mining of epidemiological data to identify outbreaks of a single disease.
Computer programmers and designers also use the study of probability and statistical data analysis to develop machines and computer programs. Google’s Internet search engine was designed using statistical data mining. Google continues to collect and use data mining to create program updates and applications.
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