*The Concept of Data Mining DOI: http://dx.doi.org/10.5772/intechopen.99417*


These methods are best applied to particular tasks in order to achieve the best performance. The **Table 1** below lists the data mining tasks and the techniques that can be used to complete them.

A business analyst's dream is data warehousing. All of the data concerning the organization's actions is centralized and accessible through a single set of analytical tools. A data warehouse system's goal is to give decision-makers the accurate, timely data they need to make the best decisions possible. A relational database management system server serves as the central repository for informational data in the data warehouse architecture. The processing of operational data is kept distinct from the processing of data warehouse data.

The central information repository is surrounded by a number of critical components that work together to make the overall ecosystem functional, manageable, and available to both operational systems and end-user query and analysis tools. The warehouse's raw data is often derived from operational applications. Data is cleansed and turned into an integrated structure and format when it enters the warehouse. Conversion, summarization, filtering, and condensing of data may all be part of the transformation process. Since the data contains a historical component, the warehouse must be capable of holding and managing large volumes of data as well as different data structures for the same database over time.


#### **Table 1.**

*Data mining tasks and the methods used to accomplish them.*


The following **Table 2** lists data mining techniques and their areas of applications.

#### **Table 2.**

*Data mining techniques and their areas use.*
