Knowledge Discovery is a set of steps aimed at producing actionable and useful knowledge from raw data that can be from multiple/large/disorganized repositories.
Knowledge Discovery (KD) involves; Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evaluation and Knowledge Representation.
a. Data Cleaning – This is the process of removing irrelevant data from a data collection.
b. Data Integration – This is the process of combining data from multiple data sources into a one data source.
c. Data Selection – This is the process of retrieving data of interest from a data collection.
d. Data Transformation – This is the process of consolidating selected data into forms that can be useful in the mining process.
e. Data Mining – This is the process of extracting useful patterns frrom the transformed data.
f. Pattern Evaluation – This is the process of using measures to identify specific interesting patterns from the mined data.
g. Knowledge Representation – This is the process of presenting knowledge to users visually for interpretation and better understanding.
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