Seven Castles

Data Mining

                                      

December 8, 2007

 

Database systems have brought digital data capture and storage to the mainstream of data processing, leading to the creation of large data warehouses, whose primary purpose is to gain access to data for analysis and decision support. However, traditional manual data analysis and exploration requires highly trained data analysts and is ineffective for high dimensionality (large numbers of variables) and massive data sets.

 

Data mining (or knowledge discovery) is the automatic extraction of useful, often previously unknown information from large databases or data sets to help reduce, model, understand or analyze the data. Its techniques are fundamentally data reduction and visualization techniques. As the number of dimensions grows, the number of possible combinations of choices for dimensionality reduction explodes.

 

The formulas used in data mining are known as algorithms. Two common data mining algorithms are regression analysis and classification analysis. Regression analysis is used with numerical data (quantitative data). If you need to work with categorical data, or a combination of categorical data and continuous numeric, classification analysis will meet your requirements.

 

The data mining process involves several steps: defining the problem, building the database, examining the data, preparing a model to be used to probe the data, testing the model, using the model and putting the results into action.

 

Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.

 

For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns and accurately predict customer loyalty. Specific uses of data mining include market segmentation, customer churn, fraud detection, direct marketing, interactive marketing, market basket analysis and trend analysis.

 

Data mining identifies key attributes of business processes, trends and target opportunities within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to determine relationships among "internal" factors such as price, product positioning or staff skills, and "external" factors such as economic indicators, competition and customer demographics, and determine the impact on sales, customer satisfaction and corporate profits.

 

Data mining is usually used by businesses, intelligence organizations and financial analysts, but is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.

【作者: zhangliping】【访问统计:】【2007年12月8日 星期六 05:57】【注册】【打印】