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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. The following figure shows data mining as a step in an iterative knowledge discovery process.
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The Knowledge Discovery in Databases process comprises of a few steps leading from raw data collections to some form of new knowledge. The iterative process consists of the following steps:
Data cleaning: also known as data cleansing, it is a phase in which noise data and irrelevant data are removed from the collection.
Data integration: at this stage, multiple data sources, often heterogeneous, may be combined in a common source.
Data selection: at this step, the data relevant to the analysis is decided on and retrieved from the data collection.
Data transformation: also known as data consolidation, it is a phase in which the selected data is transformed into forms appropriate for the mining procedure.
Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful.
Pattern evaluation: in this step, strictly interesting patterns representing knowledge are identified based on given measures.
Knowledge representation: is the final phase in which the discovered knowledge is visually represented to the user. This essential step uses visualization techniques to help users understand and interpret the data mining results.
It is common to combine some of these steps together. For instance, data cleaning and data integration can be performed together as a pre-processing phase to generate a data warehouse. Data selection and data transformation can also be combined where the consolidation of the data is the result of the selection, or, as for the case of data warehouses, the selection is done on transformed data.
Uses of Datamining
AI/Machine Learning
Combinatorial/Game Data Mining
Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess)
Business Strategies
Market Basket Analysis
Identify customer demographics, preferences, and purchasing patterns.
Risk Analysis
Product Defect Analysis
Analyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line.
User Behavior Validation
Fraud Detection
In the realm of cell phones
comparing phone activity to calling records. Can help detect calls made on cloned phones.
Similarly, with credit cards, comparing purchases with historical purchases. Can detect activity with stolen cards.
Health and Science
Protein Folding
Predicting protein interactions and functionality within biological cells. Applications of this research include determining causes and possible cures for Alzheimers, Parkinson's, and some cancers (caused by protein "misfolds")
Extra-Terrestrial Intelligence
Scanning Satellite receptions for possible transmissions from other planets.
Sources of Data for Mining:
Databases (most obvious)
Text Documents
Computer Simulations
How Can We Do Data Mining?
By Utilizing the CRISP-DM Methodology
a standard process
existing data
software technologies
situational expertise
Phases and Tasks
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Some more features of Datamining
Data Mining can be utilized in any organization that needs to find patterns or relationships in their data.
By using the CRISP-DM methodology, analysts can have a reasonable level of assurance that their Data Mining efforts will render useful, repeatable, and valid results.

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