- Database technology has evolved from primitive data processing to developing database management systems with query and transaction processing capabilities.
Further developments have resulted in a growing need for effective data analysis and data understanding tools. This need is an inevitable consequence of the explosion of
data ; these applications include business and management, administrative management, science and engineering, and environmental control.
- Data mining is the discovery of interesting patterns from large amounts of data, which can be stored in databases, data warehouses, or other information stores.
in storage. This is a young interdisciplinary field, originating from such fields as database systems, data warehousing, statistics, machine learning, data
visualization, information extraction and high performance computing. Other areas of contribution include neural networks, pattern recognition, spatial data
analysis , image databases, signal processing and several application areas including business, economics and bioinformatics.
- The knowledge discovery process includes data cleaning, data integration, data transformation, data mining, pattern evaluation and knowledge representation.
- Data patterns can be mined from different types of databases; such as relational databases, data warehouses, transactional, object-relational and faceted
object-oriented database. Interesting data patterns can also be extracted from other types of information stores, including spatial, time
-related , textual, multimedia and heritage databases, and the World Wide Web.
A data warehouse is a long-term storage of data from multiple data sources that is organized to support management decisions.
This data is housed in a consistent schema and is usually aggregated. A data warehouse provides some data analysis capabilities
called OLAP (Online Analytical Processing).
- Data mining capabilities include discovering concept/class descriptions, associations, classifications, predictions, clustering, trend analysis, bias analysis, and similarity
analyze. Characteristics and distinctions are forms of data aggregation.
- Schema provides knowledge if it is easy to understand, valid to some extent for test data, potentially useful, new
innovative, or it validates some kind of hunch the user is concerned about. Pattern interest measures, whether objective or subjective, can
be used to guide the discovery process.
- Data mining systems can be classified according to the type of database mined, the type of knowledge mined, or the techniques used.
- Effective data mining in large databases poses a large number of demands and great challenges for researchers and developers. number of questions
Data mining techniques, user interaction, performance and scalability, and processing of a large number of different data types. Other issues include
application development of data mining and their social impact.