数据仓储与数据挖掘外文文献及翻译.docx

1、数据仓储与数据挖掘外文文献及翻译外文文献：What is Data Mining?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 m

2、ining” 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 sma

3、ll set of precious nuggets from a great deal of raw material. Thus, such a misnomer which carries both “data” and “mining” became a popular choice. There are many other terms carrying a similar or slightly different meaning to data mining, such as knowledge mining from databases, knowledge extractio

4、n, data / pattern analysis, data archaeology, and data dredging.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 database

5、s. Knowledge discovery consists of an 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 transform

6、ation : 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 int

7、eresting patterns representing knowledge 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 inte

8、resting patterns are presented to the 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 knowled

9、ge discovery process. However, in industry, 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 minin

10、g functionality: data mining is the process 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.

11、 Database, data warehouse, or other information 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

12、 data warehouse server is responsible 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

13、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

14、thresholds, and metadata (e.g., describing 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 deviatio

15、n analysis.5. Pattern evaluation module. 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 eval

16、uation 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 onl

17、y the interesting patterns. 6. Graphical 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

18、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. From a data warehouse perspective, data mining can be viewed as an adva

19、nced stage of on-1ine analytical processing (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 mark

20、et, 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 statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retri

21、eval, including finding aggregate values, or that performs deductive query answering in large databases should be more appropriately categorized as either a database system, an information retrieval system, or a deductive database system. Data mining involves an integration of techniques from mult1p

22、le disciplines such as database technology, statistics, machine learning, high performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. We adopt a database perspective in our presentation of data m

23、ining in this book. That is, emphasis is placed on efficient and scalable data mining techniques for large databases. By performing data mining, interesting knowledge, regularities, or high-level information can be extracted from databases and viewed or browsed from different angles. The discovered

24、knowledge can be applied to decision making, process control, information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems and one of the most promising, new database applications in the information industry. A c

25、lassification of data mining systems Data mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other discip

26、lines may be applied, such as neural networks, fuzzy and or rough set theory, knowledge representation, inductive logic programming, or high performance computing. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques

27、 from spatial data analysis, Information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, or psychology.Because of the diversity of disciplines contributing to data mining, data mining research is expected to generate a large variety of

28、 data mining systems. Therefore, it is necessary to provide a clear classification of data mining systems. Such a classification may help potential users distinguish data mining systems and identify those that best match their needs. Data mining systems can be categorized according to various criter

29、ia, as follows. 1) Classification according to the kinds of databases mined. A data mining system can be classified according to the kinds of databases mined. Database systems themselves can be classified according to different criteria (such as data models, or the types of data or applications invo

30、lved), each of which may require its own data mining technique. Data mining systems can therefore be classified accordingly. For instance, if classifying according to data models, we may have a relational, transactional, object-oriented, object-relational, or data warehouse mining system. If classif

31、ying according to the special types of data handled, we may have a spatial, time -series, text, or multimedia data mining system , or a World-Wide Web mining system . Other system types include heterogeneous data mining systems, and legacy data mining systems.2) Classification according to the kinds

32、 of knowledge mined.Data mining systems can be categorized according to the kinds of knowledge they mine, i.e., based on data mining functionalities, such as characterization, discrimination, association, classification, clustering, trend and evolution analysis, deviation analysis , similarity analy

33、sis, etc. A comprehensive data mining system usually provides multiple and/or integrated data mining functionalities. Moreover, data mining systems can also be distinguished based on the granularity or levels of abstraction of the knowledge mined, including generalized knowledge(at a high level of abstraction), primitive-