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Data-warehousing-and-data-mining-10IS74-10CS755-->View question

What is data mining Explain Data Mining and Knowledge Discovery?

data mining

Data Mining and Knowledge Discovery



Asked On2017-05-17 18:15:35 by:pallaviaithaln

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Simply stated, data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named knowledge mining from data, which is unfortunately somewhat
long. Knowledge mining, a shorter term, may not reflect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that finds a small set of precious nuggets from a great deal of raw material (Figure 1.3). Thus, such a misnomer that carries both data and mining became a popular choice. Many other terms
carry a similar or slightly different meaning to data mining, such as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging.
Many people treat data mining as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD. Alternatively, others view data mining as simply an evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. Knowledge such as user beliefs, which can be used to assess a patterns interestingness based on its unexpectedness, may also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources).
Data mining engine: This is essential to the data mining system and ideally consists of a set of functional modules for tasks such as characterization, association and correlation analysis, classification, prediction, cluster analysis, outlier analysis, and evolution analysis.
Pattern evaluation module: This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search toward interesting patterns. It may use interestingness thresholds to filter out discovered patterns. Alternatively, the pattern evaluation module may be integrated with the mining module, depending on the implementation
of the data mining method used. For efficient data mining, it is highly recommended to push the evaluation of pattern interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns.
User interface: This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the
intermediate data mining results. In addition, this component allows the user to browse database and data warehouse schemas or data structures, evaluate mined patterns, and visualize the patterns in different forms.


Answerd on:2015-01-20 Answerd By:manjarimattur

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Simply stated, data mining refers to extracting or mining knowledge from large amounts of data
The term is actually a misnomer.
Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named knowledge mining from data, which is unfortunately somewhat long.
Knowledge mining, a shorter term, may not reflect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that finds a
small set of precious nuggets from a great deal of raw material .Thus, such a
misnomer that carries both data and mining became a popular choice. Many other terms
carry a similar or slightly different meaning to data mining, such as knowledge mining from
data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging.
Many people treat data mining as a synonym for another popularly used term, Knowledge
Discovery from Data, or KDD. Alternatively, others view data mining as simply an evaluate the
interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to
organize attributes or attribute values into different levels of abstraction. Knowledge such as user
beliefs, which can be used to assess a patterns interestingness based on its unexpectedness, may
also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources)

Data mining engine: This is essential to the data mining system and ideally consists of a set of
functional modules for tasks such as characterization, association and correlation analysis,
classification, prediction, cluster analysis, outlier analysis, and evolution analysis.
Pattern evaluation module: This component typically employs interestingness measures and
interacts with the data mining modules so as to
focus
the search toward interesting patterns. It
may use interestingness thresholds to filter out discovered patterns. Alternatively, the pattern
evaluation module may be integrated with the mining module, depending on the implementation
of the data mining method used. For efficient data mining, it is highly recommended to push the
evaluation of pattern interestingness as deep as possible into the mining process so as to confine
the search to only the interesting patterns.
User interface: This module communicates between users and the data mining system, allowing
the user to interact with the system by specifying a data mining query or task, providing
information to help focus the search, and performing exploratory data mining based on the
intermediate data mining results. In addition, this component allows the user to browse database
and data warehouse schemas or data structures, evaluate mined patterns, and visualize the
patterns in different forms.


Answerd on:2015-01-20 Answerd By:pallaviaithaln

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