Data mining software analyzes stored transaction data to identify relationships and patterns. It can group data into classes, clusters, or identify associations and sequential patterns. Data mining is used to predict trends, discover previously unknown patterns, and drive business decisions for marketing, finance, manufacturing, and government. However, privacy issues arise from personal data collection and security issues from data theft, requiring proper handling of private information.

2.  analyzing data from different perspectives and
summarizing it into useful information .
 Data mining software is one of a number of analytical tools
for analyzing data.
It can be used to increase revenue, cuts costs, or both.
correlations or patterns among dozens of fields in large
relational databases.

3. process of centralized data management and retrieval.
represents an ideal vision of maintaining a central
repository of all organizational data.
Dramatic advances in data capture, processing power, data
transmission, and storage capabilities are enabling
organizations to integrate their various databases

4. determine relationships among "internal" factors such as price,
product positioning, or staff skills & “external" factors such as economi
indicators, competition, and customer demographics.
determine the impact on sales, customer satisfaction, and corporate
profits.
determine the impact on sales, customer satisfaction, and corporate
profits.
WalMart is pioneering massive data mining to transform its supplier
relationships. WalMart captures point-of-sale transactions from over
2,900 stores in 6 countries and continuously transmits this data to its
massive 7.5 terabyte Teradata data warehouse.

5. Data mining software analyzes relationships and patterns in stored
transaction data based on open-ended user queries.
four types of relationships are sought:
Classes: Stored data is used to locate data in predetermined groups.
Clusters: Data items are grouped according to logical relationships or
consumer preferences.
Associations: Data can be mined to identify associations.
Sequential patterns: Data is mined to anticipate behavior patterns and
trends.

6. Data mining derives its name from the similarities between searching for valuable
business information in a large database.
Automated prediction of trends and behaviors.
finding predictive information in large databases. predictive problem is targeted marketing.
uses data on past promotional mailings to identify the targets most likely to maximize return on
investment in future mailings. include forecasting bankruptcy and other forms of default, and
identifying segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns.
identify previously hidden patterns in one step. An example of pattern discovery is the analysis
of retail sales data to identify seemingly unrelated products that are often purchased together.
Other pattern discovery problems include detecting fraudulent credit card transactions and
identifying anomalous data that could represent data entry keying errors.

7. •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 mining consists of five major elements:

8. Benefits of Data Mining
Marketing / Retail
Data mining helps marketing companies build models based on historical data to predict who
will respond to the new marketing campaigns such as direct mail, online marketing
campaign…etc. Through the results, marketers will have an appropriate approach to selling
profitable products to targeted customers.
Finance / Banking
Data mining gives financial institutions information about loan information and credit
reporting. In addition, data mining helps banks detect fraudulent credit card transactions to
protect credit card’s owner.
Manufacturing
By applying data mining in operational engineering data, manufacturers can detect faulty
equipment and determine optimal control parameters.
Governments
Data mining helps government agency by digging and analyzing records of the financial
transaction to build patterns that can detect money laundering or criminal activities.

9. Impact OfData Mining
Privacy Issues
Because of privacy issues, people are afraid of their personal information is collected and used
in an unethical way that potentially causing them a lot of troubles. Businesses collect
information about their customers in many ways for understanding their purchasing behaviors
trends. At this time, the personal information they own probably is sold to other or leak.
Security issues
Security is a big issue. Businesses own information about their employees and customers. There
have been a lot of cases that hackers accessed and stole big data of customers from the big
corporation. with so much personal and financial information available, the credit card stolen
and identity theft become a big problem.
Misuse of information/inaccurate information
Information is collected through data mining intended for the ethical purposes can be misused.

10. Data mining brings a lot of benefits to businesses, society, governments
as well as the individual. However, privacy, security, and misuse of
information are the big problems if they are not addressed and resolved
properly. A new technological leap is needed to structure and prioritize
information for specific end-user problems. The data mining tools can
make this leap. Quantifiable business benefits have been proven
through the integration of data mining with current information
systems, and new products are on the horizon that will bring this
integration to an even wider audience of users.