Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.
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Instead of addressing the problem from a traditional knowledge discovery perspective by attempting to discover interesting patterns in sequences of events, sequential pattern extraction aims to efficiently sieve through large volumes of data and locate sets of events exhibiting some predefined correlation relationship.
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Data mining parameters include:
- Association - looking for patterns where one event is connected to another event
- Sequence or path analysis - looking for patterns where one event leads to another later event
- Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok)
- Clustering - finding and visually documenting groups of facts not previously known
- Forecasting - discovering patterns in data that can lead to reasonable predictions about the future (This area of data mining is known as predictive analytics)
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Data mining uses discovery-based approaches in which pattern-matching and other algorithms are employed to determine the key relationships in the data. Data mining algorithms can look at numerous multidimensional data relationships concurrently, highlighting those that are dominant or exceptional.