Difference between (Knowledge Discovery in Databases)and datamining or, KDD versus Data Mining.

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 Differentiate between KDD and Data mining.

KDD is a field of computer science, which deals with the extraction of previously unknown and interesting information from raw data. KDD is the whole process of trying to make sense of data by developing appropriate methods or techniques. This process deal with the mapping of low-level data into other forms that are more compact, abstract, and useful. This is achieved by creating short reports, modeling the process of generating data, and developing predictive models that can predict future cases. Due to the exponential growth of data, especially in areas such as business, KDD has become a very important process to convert this large wealth of data into business intelligence, as manual extraction of patterns has become seemingly impossible in the past few decades.

Data Mining is only a step within the overall KDD process. There are two major Data Mining goals as defined by the goal of the application, and they are namely verification or discovery. Verification is verifying the user's hypothesis about data, while discovery is automatically finding interesting patterns. There are four major data mining tasks: clustering, classification, regression, and association (summarization). Clustering is identifying similar groups from unstructured data. Classification is learning rules that can be applied to new data. Regression is finding functions with minimal error to model data. And the association is looking for relationships between variables.

Although the two terms KDD and Data Mining are heavily used interchangeably, they refer to two related yet slightly different concepts. KDD is the overall process of extracting knowledge from data while Data Mining is a step inside the KDD process, which deals with identifying patterns in data. In other words, Data Mining is only the application of a specific algorithm based on the overall goal of the KDD process. 

KDD versus Data Mining

KDD (Knowledge Discovery in Databases) is a field of computer science, which includes the tools and theories to help humans in extracting useful and previously unknown information (i.e. knowledge) from large collections of digitized data. 

KDD consists of several steps, and Data Mining is one of them. Data Mining is the application of a specific algorithm in order to extract patterns from data. Nonetheless, KDD and Data Mining are used interchangeably. In summary, Data Mining is only the application of a specific algorithm based on the overall goal of the KDD process.



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