'If thou art able, O stranger,
to find out all these things and gather them together in your mind,
giving all the relations, thou shalt depart crowned with glory and knowing
that thou hast been adjudged perfect in this species of wisdom.'
Data mining is the art and science of extracting hidden patterns from the accumulated data for decision-making.It has emerged as a valuable decision support tool with the recognition that:
The three essential requisites of good data mining initiatives are:
Data mining projects require substantial initial effort in data preparation.
Research indicates that 75% of total project time goes in data preparation.
Many companies embark on data warehousing initiatives without first
developing a data mining vision. Developing a
data warehousing solution in the context and background
of a data mining vision increases the value of the data warehousing
Data mining is used mostly in applications where a large amount of data is generated/available. The typical sectors are Finance, Insurance, Banking, Retail, Telecommunications, Airlines, Public Utilities, etc.Data MiningTechniques:
1. Artificial Neural Networks:
Non-linear predictive models that learn through training, and resemble biological neural networks in structure.
2. Decision Trees:
Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID)
3. Genetic Algorithms:
Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.
4. Nearest Neighbor Method:
A technique that classifies each record in a dataset based on a combination of the classes of k record(s) most similar to it in a historical data set.
5. Rule Induction:
The extraction of useful if-then rules from data based on statistical significance.
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