There are a number of systems for analyzing data and the best one for the job depends on the type of data being analyzed and the results required at the end. There are three main types of data: Categorical data, quantitative data which is a number and qualitative data which is the presence or absence of a certain characteristic.
Data mining is a type of data analysis which has become very popular with the improvements in computer technology. It is often used in business, especially consumer focus business, to find patterns and correlations in huge quantities of digital data for everything from marketing and product positioning to fraud detection. Data used in data mining includes transactional sales, payroll, wider industry sales and even data on the data.
Data mining software is usually used to determine any four types of relationships. These are clusters, sequential patterns, associations and classes. Classes refer to predetermined groups and an example would be a clothes shop mining their purchase data to determine when customers most bought a certain type of clothing to help with targeted promotions.
Clusters of data can be used to identify market segments and associative mining identifies associations that might not otherwise be obvious. For example the famous instance of men being targeted with beer promotions near the diapers in a supermarket, as it was found a high volume of men were coming in late at night to buy diapers.
Sequential patterns are used to predict behaviour trends when analyzing data. For example a retailer could predict that a customer will buy a tent based on the purchase of a camping stove and sleeping bags.
Data mining is a type of data analysis which has become very popular with the improvements in computer technology. It is often used in business, especially consumer focus business, to find patterns and correlations in huge quantities of digital data for everything from marketing and product positioning to fraud detection. Data used in data mining includes transactional sales, payroll, wider industry sales and even data on the data.
Data mining software is usually used to determine any four types of relationships. These are clusters, sequential patterns, associations and classes. Classes refer to predetermined groups and an example would be a clothes shop mining their purchase data to determine when customers most bought a certain type of clothing to help with targeted promotions.
Clusters of data can be used to identify market segments and associative mining identifies associations that might not otherwise be obvious. For example the famous instance of men being targeted with beer promotions near the diapers in a supermarket, as it was found a high volume of men were coming in late at night to buy diapers.
Sequential patterns are used to predict behaviour trends when analyzing data. For example a retailer could predict that a customer will buy a tent based on the purchase of a camping stove and sleeping bags.
