Data mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the internet, and other sources. It utilizes mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD).
How Data Mining works
Data mining involves six common classes of tasks:
- Anomaly detection: The identification of unusual data records, that might be interesting or data errors.
- Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes.
- Clustering: Is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification: This is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as "legitimate" or as "spam".
- Regression: Attempts to find a function which models the data with the least error.
- Summarization: Providing a more compact representation of the data set, including visualization and report generation.
Data mining helps organizations to make the profitable adjustments in operation and production. The data mining process includes several steps: raw data collection, data management, data analysis, forecasting and model tracking.
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