Data
mining is the analysis step of the “knowledge discovery in databases”
process, or KDD. The term is a misnomer, because the goal is the extraction of
patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. The
overall goal of the data mining process is to extract information from a data
set and transform it into an understandable structure for further use.
Generally, data mining (sometimes called data or knowledge discovery) is the
process of analyzing data from different perspectives and summarizing it into
useful information. Data mining can also be described as sorting through data
to identify patterns and establish relationships.
mining is the analysis step of the “knowledge discovery in databases”
process, or KDD. The term is a misnomer, because the goal is the extraction of
patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. The
overall goal of the data mining process is to extract information from a data
set and transform it into an understandable structure for further use.
Generally, data mining (sometimes called data or knowledge discovery) is the
process of analyzing data from different perspectives and summarizing it into
useful information. Data mining can also be described as sorting through data
to identify patterns and establish relationships.
Data
mining parameters include:
mining parameters include:
- Association – looking for patterns where one
event is connected to another event - Sequence or path
analysis – looking for patterns where one event leads to another later
event - Classification –
looking for new patterns (May result in a change in the way the data is
organized but that’s ok) - Clustering – finding
and visually documenting groups of facts not previously known - Forecasting –
discovering patterns in data that can lead to reasonable predictions about
the future
