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data-reduction
Meaning to reduce the volume of data while maintaining its integrity. This is important because large datasets can be time-consuming and expensive to analyze.
Dimensionality reduction** : Removing irrelevant or redundant attributes.
Numerosity reduction** : Using methods such as regression or clustering to summarize data into fewer data points.
Data compression** : Reducing the size of the dataset without losing important information.
Status: #idea
Tags: data-mining, kdd, data-prepartion
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data-transformation
Meaning to convert the data into a suitable format for mining.
* Normalization : Scaling data to fit within a specific range (e.g., between 0 and 1).
* Discretization \\\\: Dividing continuous attributes into intervals or categories.
* Attribute/feature construction : Creating new attributes from existing ones to improve the mining process.
Status: #idea
Tags: data-mining, kdd, data-prepartion
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data-warehousing
OLTP
OLAP
Status: #idea
Tags: data-mining
References
fault tolerant
Can stop and start at any time
kdd
data-prepartion
data mining
pattern-evaluation
knowledge-presentation
Status: #idea
Tags: data-mining
References
Pay as you go pricing model
* ï»żï»żNo upfront costs and pay for what you use
* ï»żï»żScale resources as needed for cost optimization
Status: #idea
Tags: az-900, azure, Cloud, cloud-billing, ccp, aws, gcp, google-cloud-engineer
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