Chapter: Data Warehousing and Data Mining

Data Mining

Data mining refers to extracting or ―mining‖ knowledge from large amounts of data.





Data mining refers to extracting or ―mining‖ knowledge from large amounts of data.


Many other terms carry a similar or slightly different meaning to data mining, such as knowledge mining from data, knowledge extraction, data/pattern analysis, another popularly used term, Knowledge Discovery from Data, or KDD.


Essential step in the Process of knowledge discovery. Knowledge discovery as a process is depicted in Figure consists of an iterative sequence of the following steps:


Data cleaning: to remove noise and inconsistent data

Data integration: where multiple data sources may be combined

Data selection: where data relevant to the analysis task are retrieved from the database


Data transformation: where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance


Data mining: an essential process where intelligent methods are applied in order to extract data patterns


Pattern evaluation to identify the truly interesting patterns representing knowledge based on some interestingness measures;


Knowledge presentation where visualization and knowledge representation techniques are used to present the mined knowledge to the user


The architecture of a typical data mining system may have the following major components Database, data warehouse, Worldwide Web, or other information repository: This is one or a set of databases, data warehouses, spreadsheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data.

Database or data warehouse server: The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.


Knowledge base: This is the domain knowledge that is used to guide the search or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. 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..


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.

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