Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. While data mining and knowledge discovery in databases (or KDD) are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
Statisticians were the first to use the term “data mining.” Originally, “datamining” or “data dredging” was a derogatory term referring to attempts toextract information that was not supported by the data.
Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization.
Data mining derives its name from the similarities between searching for valuable information in a large database and mining rocks for a vein of valuable ore. Both imply either sifting through a large amount of material or ingeniously probing the material to exactly pinpoint where the values reside.
The essential difference between the data mining and the traditional data analysis (such as query, reporting and on-line application of analysis) is that the data mining is to mine information and discover knowledge on the premise of no clear assumption.
Data mining is highly useful in the following domains:
*Market Analysis and Management
*Corporate Analysis & Risk Management
*Fraud Detection
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid.
Stages of the Data Mining Process
1. Data gathering
2. Data cleansing
3. Feature extraction
4. Pattern extraction and discovery
5. Visualization of the data.
6. Evaluation of results
Data mining
