外文文献及翻译.docx

1、外文文献及翻译What is Data Mining?Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases”, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery consists of an

2、 iterative sequence of the following steps: data cleaning: to remove noise or irrelevant 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

3、 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 knowle

4、dge based on some interestingness measures, and knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user . The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the

5、 user, and may be stored as new knowledge in the knowledge base. Note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers hidden patterns for evaluation. We agree that data mining is a knowledge discovery process. However, in ind

6、ustry, in media, and in the database research milieu, the term “data mining” is becoming more popular than the longer term of “knowledge discovery in databases”. Therefore, in this book, we choose to use the term “data mining”. We adopt a broad view of data mining functionality: data mining is the p

7、rocess of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Based on this view, the architecture of a typical data mining system may have the following major components: 1. Database, data warehouse, or other i

8、nformation repository. This is one or a set of databases, data warehouses, spread sheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data. 2. Database or data warehouse server. The database or data warehouse server is responsible

9、 for fetching the relevant data, based on the users data mining request. 3. 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 v

10、alues 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., descr

11、ibing data from multiple heterogeneous sources).4. 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 analysis, classification, evolution and deviation analysis.5. Pattern evaluation modu

12、le. This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness thresholds stored in the knowledge base. Alternatively, the pattern evaluation module may be integrated with

13、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. 6. Graphi

14、cal 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

15、 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. From a data warehouse perspective, data mining can be viewed as an advanced stage of on-1ine analytical proc

16、essing (OLAP). However, data mining goes far beyond the narrow scope of summarization-style analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding. While there may be many “data mining systems” on the market, not all of them can perform true data mining. A data analysis system that does not handle large amounts of data can at most be categorized as a machine learning system, a statis