data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. 

## 1.2 What Can Data Mining Do?

Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies 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, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. 

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures. 

## 1.3 How Does Data Mining Work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: 
 **Classes**: Stored data is used to locate data in predetermined 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**: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. 
 **Sequential patterns**: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes. 

Data mining process includes various internal operations as shown in Fig 1

![0_image_0.png](0_image_0.png)

## 1.3.1 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. 