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Data Mining

Data Mining is the process of searching,sorting and analyzing raw data from different prospectives to identify and discover patterns and establish relationship within data into useful information.Data mining uses sophisticated mathematical algorithms, artificial intelligence techniques, neural networks and advanced statistical tools to segment the data and evaluate the probability of future events which might otherwise have remain undetected. Data mining discover results depending how the patterns are formulated and defined. The solution cannot be automatically addressed through simple query and reporting techniques.By using software to look for patterns in large batches of data.Data mining depends on effective data collection and warehousing and computer processing.

The Key Properties of Data Mining are

Automatic Discovery (Scoring) - By building models using algorithm helps in data mining.

Prediction - Some forms of predictive data mining creates rules,which are conditions that imply a given output.

Grouping - Data mining identifies natural grouping in the data.

Data Mining and Statistics - Statistical methods and techniques used in data mining.

Data Mining and OLAP - In data mining OLAP(On-Line Analytical Processing) techniques are used for different forms of analysis of data.

Data Mining and Data Warehousing - Data is stored in organised way by Data Warehousing and for prediction and problem resolution data mining model is used.

Data Mining Process -
Problem Definition.
Data gathering and preparation.
Model Building and Evaluation.
Different models are used for modeling.
Descriptive Modeling - It uncovers shared similarities or groupings in historical data.
Predictive Modeling - This modeling goes deeper to classify events in the future or estimate unknown outcomes.
Prescriptive Modeling - With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining. Knowledge deployment