🗒️ Explore my blog

properly preparing the data to ensure that it is clean, consistent, and ready for analysis. data-cleaning data-integration data-transformation data-reduction Status: #idea Tags: data-mining, kdd References

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 Refe

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 References

OLTP OLAP Status: #idea Tags: data-mining References

Can stop and start at any time

data-prepartion data mining pattern-evaluation knowledge-presentation Status: #idea Tags: data-mining References