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Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue.
Data mining is also known as data discovery and knowledge discovery.
The major steps involved in a data mining process are:
The first step in data mining is gathering relevant data critical for business. Company data is either transactional, non-operational or metadata. Transactional data deals with day-to-day operations like sales, inventory and cost etc. Non-operational data is normally forecast, while metadata is concerned with logical database design. Patterns and relationships among data elements render relevant information, which may increase organizational revenue. Organizations with a strong consumer focus deal with data mining techniques providing clear pictures of products sold, price, competition and customer demographics.
For instance, the retail giant Wal-Mart transmits all its relevant information to a data warehouse with terabytes of data. This data can easily be accessed by suppliers enabling them to identify customer buying patterns. They can generate patterns on shopping habits, most shopped days, most sought for products and other data utilizing data mining techniques.
The second step in data mining is selecting a suitable algorithm - a mechanism producing a data mining model. The general working of the algorithm involves identifying trends in a set of data and using the output for parameter definition. The most popular algorithms used for data mining are classification algorithms and regression algorithms, which are used to identify relationships among data elements. Major database vendors like Oracle and SQL incorporate data mining algorithms, such as clustering and regression tress, to meet the demand for data mining.