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data mining full report
#31
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#32
What is Data Mining?


.ppt   1Data Mining.ppt (Size: 811.5 KB / Downloads: 58)

Results of Data Mining Include:

Forecasting what may happen in the future
Classifying people or things into groups by recognizing patterns
Clustering people or things into groups based on their attributes
Associating what events are likely to occur together
Sequencing what events are likely to lead to later events

Data mining is not

Brute-force crunching of bulk data
“Blind” application of algorithms
Going to find relationships where none exist
Presenting data in different ways
A database intensive task
A difficult to understand technology requiring an advanced degree in computer science

Data Mining Is

A hot buzzword for a class of techniques that find patterns in data
A user-centric, interactive process which leverages analysis technologies and computing power
A group of techniques that find relationships that have not previously been discovered
Not reliant on an existing database
A relatively easy task that requires knowledge of the business problem/subject matter expertise

Why CRISP-DM?

The data mining process must be reliable and repeatable by people with little data mining skills
CRISP-DM provides a uniform framework for
guidelines
experience documentation
CRISP-DM is flexible to account for differences
Different business/agency problems
Different data

Final Comments

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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#33
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#34
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#35

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