Intelligent Agent / Network Mining

1. Intelligent Agent / Network Mining Group 4 Ali – Katy – Sahar

2. Intelligent Agent (IA) • is an independent entity which observes and acts upon an environment and directs its activity towards achieving goals. • autonomous programs used for operator assistance or data mining are also called "intelligent agents". • a reflex machine such as a thermostat is an intelligent agent

3. Network Mining • By "Network Mining" we mean Data Mining of data available within a network environment. • Real world data obtained from the local Cable Internet provider are analyzed to find rules to help better manage the network. • This large dataset helps validate the use of data mining techniques for rule generation. of the rules generated, to be passed on to the network managers, some proved to be interesting.

4. Data – Information – Knowledge • What is Data? • What is Information? • What is knowledge?

5. Data warehouse • Data warehousing is defined as a process of centralized data management and retrieval. • Centralization of data is needed to maximize user access and analysis. • Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. • The data analysis software is what supports data mining.

6. Tools : Data Mining • is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. • Data mining is the process of extracting patterns from data. • Data mining techniques are the result of a long process of research and product development. • Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations.

7. It enables the 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. • It enables them to determine the impact on sales, customer satisfaction, and corporate profits.

8. Data Mining examples • A popular example of data mining is using past behavior to rank customers. • Such tactics have been employed by financial companies for years as a means of deciding whether or not to approve loans and credit cards. • 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.

9. one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. • They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. • Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. • The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue.