Data Mining
Data Mining is the process of automatically searching large volumes of data for patterns. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
Five Major Elements in Data Mining
Data mining consists of five major elements:
Data Relationships
A clear understanding of the data is important for determining how to exploit the information for business purposes. One way to understand data is to create one or more of the following related groups:
Data Classes
Data classes are groups that share easily identifiable characteristics.
Data Clusters
Data clusters are similar to classes, but include additional attributes such as logical relationships. In the context of business applications
Data Associations
Data associations take clusters further. In the context of business application, associative data mining reveals buying patterns that would otherwise go unnoticed.
Sequential patterns
While analyzing past purchases is helpful, some experts believe that the true benefit of data mining is to anticipate customer purchases through predictive analytics.
Applications of Data Mining
Data Mining is the process of automatically searching large volumes of data for patterns. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
Five Major Elements in Data Mining
Data mining consists of five major elements:
- Extract, transform, and load transaction data onto the data warehouse system.
- Store and manage the data in a multidimensional database system.
- Provide data access to business analysts and information technology professionals.
- Analyze the data by application software.
- Present the data in a useful format, such as a graph or table.
Data Relationships
A clear understanding of the data is important for determining how to exploit the information for business purposes. One way to understand data is to create one or more of the following related groups:
Data Classes
Data classes are groups that share easily identifiable characteristics.
Data Clusters
Data clusters are similar to classes, but include additional attributes such as logical relationships. In the context of business applications
Data Associations
Data associations take clusters further. In the context of business application, associative data mining reveals buying patterns that would otherwise go unnoticed.
Sequential patterns
While analyzing past purchases is helpful, some experts believe that the true benefit of data mining is to anticipate customer purchases through predictive analytics.
Applications of Data Mining
- Banking: loan/credit card approval predict good customers based on old customers.
- Customer relationship management:identify those who are likely to leave for a competitor.
- Targeted marketing: identify likely responders to promotions
- Fraud detection: telecommunications, financial transactions from an online stream of event identify fraudulent events
- Manufacturing and production: automatically adjust knobs when process parameter changes