Hello Friends, In this blog post(KDD vs Data Mining) we are going to discuss the difference between KDD and Data mining. The full form of KDD is knowledge discovery in the database.
Knowledge discovery in database (KDD) is formalized in 1989. With reference to the general concept of being broad and high level in the pursuit of seeking knowledge from data.|KDD vs Data Mining|
The term data mining was then coined; this high-level application approach is employed to present and analyze data for decision-makers.|KDD vs Data Mining|
Data mining is among such important steps involved in the knowledge discovery process that encompasses data selection, data cleaning, and preprocessing,…
… data transformation and reduction, data algorithm choice, and finally post-processing and the interpretation of the discovered knowledge.
The KDD process tends to be highly iterative and interactive. Data mining analysis tends to work up from the data and best techniques are developed with an orientation towards large volumes of data…
…, making use of as much data as possible to arrive at reliable conclusions and decisions.
The analysis process starts with a set of data and uses a methodology to develop an optimal representation of the structure of data, during which knowledge is acquired.
Once knowledge is acquired, this can extend to large sets of data on the assumption that the large data set has a structure similar to the simple data set.
Fayyad et al distinguish between KDD and data mining by giving the following definition. knowledge discovery in the database in the process of identifying a valid potentially useful and ultimately understandable structure in data.
This process involves selecting or sampling data from a data warehouse, selecting or preprocessing it, transforming or reducing it, applying a data mining component to produce a structure, and then evaluating the derived structure.
Data mining is a step in the KDD process related to the algorithmic means by which patterns or structures are counted from the data under acceptable computational efficiency limitations.
Thus, the formats that are the outcome of the data mining process must meet certain conditions so that these can be considered knowledge. These conditions are validity understandability, utility, novelty, and interestingness.
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