Data Mining VS KDD: What's the difference and why it matters

Data mining and Knowledge Discovery in Databases (KDD) are two closely
related concepts that are often used interchangeably. However, there are
some key differences between the two that are important to understand.

Data Mining vs. KDD

Data Mining	KDD(Knowledge Discovery in Databases)
Data mining is the process of discovering patterns and knowledge from large amounts of data. It is a multidisciplinary field that combines elements of computer science, statistics, and domain expertise in order to extract useful information from data.	Knowledge Discovery in Databases (KDD) is a more comprehensive process that includes data mining as one of its key components.
Data mining is often used to identify patterns, trends, and relationships in data that can be used to make predictions or decisions.	KDD is a process that encompasses the entire cycle of extracting useful information from data, including data preparation, data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge representation.
Data mining is typically focused on the discovery of patterns and relationships in data.	KDD is focused on the entire process of discovering knowledge from data.
Data mining typically extracts numerical or categorical information.	KDD extracts both numerical and symbolic information.
data mining is often used in business and industry to identify patterns and relationships in data that can be used to improve decision making or gain a competitive advantage.	KDD, on the other hand, is often used in scientific research to extract knowledge from large amounts of data.
Data mining is a key component of the KDD process and is used to extract useful information from data.	KDD is a more comprehensive approach that includes data mining and additional steps such as data preparation, data cleaning, and knowledge representation.

It's also worth noting that the term "KDD" is not as commonly used these days, the field has progressed and evolved to include a wider range of techniques and approach, many of which have been subsumed under the broader field of "Data Science" or "Machine Learning".

In practice, it is often difficult to separate data mining from KDD, as the two concepts are closely related and often used together. Many data mining tools and techniques are also used in KDD, and many KDD tools and techniques are also used in data mining.

In conclusion, data mining and KDD are two closely related concepts that are often used interchangeably. Data mining is focused on the discovery of patterns and relationships in data, while KDD is focused on the entire process of discovering knowledge from data. Both are important tools that can be used to extract valuable insights from large amounts of data, but they have different scopes, applications, and goals.

Reference Books

Here are the books I’ve used as references for writing this article,
please feel free to read them If you don’t want your knowledge to be
limited to this article alone.

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