While large-scale information technology has developed separate transaction and systems analysis, data mining provides the connection between the two. Data mining software analyzes the relationships and patterns of stored transaction data is based on open-ended questions. Different types analytical software applications available: statistics, machine learning, and neural networks.
In general, any of the Four Type Relations is designed to:
- Classes: saved data is used to locate data in predefined groups.
For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
- Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
• Associations: The data can be mined for the identification of compounds. The beer- nappy example is an example of one of the clubs of mines.
• Sequential Patterns: The data is the minefields which provide behaviour patterns and trends. For example, an outdoor equipment retailer could provide for the possibility a backpack that is purchased on the basis of the consumer to purchase sleeping bags and hiking shoes.