Data Mining

REVIEW ON DATA MINING

Poornima D

Jiawei Han Micheline Kamber

Abstract:

My main research involved how Data Mining process involves in real world and the reach of data mining and utilities of data mining. Data Mining has a great deal of attention due to wide availability of huge amounts of data, where these imminent need for turning such data into a useful information and knowledge. From the last development of data collection and database creation mechanisms served as prerequisite from development of effective mechanisms for data storage and retrieval, and query and transaction processing. The fast-growing, tremendous-amount of data, collected and stored in large and numerous data repositories, has far exceeded our human ability for comprehension

A Pattern is interesting if it is

1. Easily understood by humans

2. Valid on new or test data with some degree of certainty.

3. Potentially useful and

4. Novel Can a Data Mining system generate all of the interesting patterns?

It is often unrealistic and efficient for data mining systems to generate all the possible patterns. Instead, User-Provided constraints and interestingness measures should be used to focus the search.

Can a Data Mining system generate only interesting patterns?

It is an optimization problem in Data Mining. it is highly desirable for data mining systems to generate only interesting patterns. This would be much more efficient for user and data mining systems, because neither would have to search through the patterns generated in order to identify the truly interesting ones. Major Issues in Data Mining:

Mining different kinds of knowledge in database:

Because different users can be interested in different kinds of knowledge. Data mining should cover a wide spectrum of data analysis and knowledge discovery tasks.

Interactive mining of knowledge at multiple levels of

The high-level language should be integrated with a database or data warehouse query language and optimized for efficient and flexible data mining.

Presentation and visualization of data mining results:

Discovered knowledge should be expressed in high-level language, visual representations or other expressive forms for easier understanding and directly usable by human.

Handling noisy or incomplete data:

Data stored in a database may reflect noise; as a result the accuracy of the discovered pattern can be poor. Data cleaning methods, data analysis methods, outlier mining methods can handle noise.

Pattern Evaluation-The interestingness problem:

A data mining system can uncover thousands of patterns. Many of the patterns discovered may be interesting to the given user, either because they represent common knowledge or lack novelty.