Difference Between Data mining vs Data Profiling
Learn and Understand the complete detail about the difference between Data Mining vs Data Profiling
What is Data Mining:
Data Mining refers to the process of identifying patterns in a pre-built database. It analyses or discovers knowledge in the existing databases and large datasets to convert raw data into useful information and to search for trends and patterns in it.
What is Data Profiling:
Data Profiling, on the other hand, also analyzes the raw data of existing datasets, but only to collect statistics or information summaries about the data. Also called data archeology, data profiling is used to derive information about the data itself and to assess the quality of the data. There are different ways of conducting data profiling in your organization such as medium, minimum, maximum, format, percentage, etc.
Data Mining and Data Profiling Techniques:
Some common techniques of data mining are association learning, clustering, classification, forecasting, sequencing patterns, regression, and much more.
- Association learning is the most commonly used technique where relationships are used to identify patterns. It is also called the relationship technique.
- Classification technique classifies items or variables in a data set in default groups or classes.
- The clustering technique creates a cluster of meaningful objects that share identical features.
The different kinds of data profiling are:
- Structure discovery or structure analysis ensures that data is consistent and accurate.
- On the other hand, content discovery looks more closely at individual elements of a database.
- Relationship discovery analyzes the type of data used to gain a better understanding of the interactions between datasets.
Data Mining vs Data Profiling
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