data mining造句
1. Data mining is used to analyse individuals' buying habits.
2. The growth of data mining has led many to worry about invasions of privacy by overzealous marketers.
3. Cluster analysis is an important technology in data mining.
4. Spatio-temporal data mining is an important research topic in data mining, and in which spatiotemporal forecasting is the most widely used.
5. Primary research results show that such data mining methods as clustering, classification, association, time-series analysis and outlier analysis are feasible in the FDD of LRE.
6. Aiming to web document classification in data mining, a classification method is presented in this paper. The method is based on vector space model and parallel connection BP neural network.
7. In succession, the technology of incremental data mining based on Rough Set theory is worked over.
8. Data mining is an intercrossed subject, involving many fields such as machine learning, model reorganization, induction and deduction, statistics, database and high performance calculation.
9. The extension method enriches the content of data mining, and provides new tools for building multivalue correlative criteria.
10. Absrtact: Text mining uses the data mining technique to find and extract the crytic knowledge automatically from text files, which is self - existent the information users needed.
11. Firstly, we overview the recent development of data mining and data classification.
12. Decision tree classifier is an important data mining problem. The key issue in constructing the decision tree on data streams is to derive the best criterion of internal nodes.
13. The document is a group of data mining areas, cluster analysis of the algor ...
14. Data mining is the core topic of this paper. Basically, it includes associate rule founding, data clustering and data assorting.
15. The basic step of the data mining has five stages: question definition and subject analysis, data preparation, model build, mode assessment, appraisal and verification of the data mining result.
16. This paper introduces data mining technology, its application in medical care system, and data mining application in Gravida and Puerpera Management Information system.
17. This topic in development uses data warehouse and data mining technology.
18. Reorganize original traffic volume data collected by traffic data collect system with data mining techniques.
19. In this paper we give an archetypal design for data mining system, Incremental updating technique is applied in this archetypal system which quickly dealing with large databases.
20. Tolerence operators are kind of operations converting general relation to compatibility relation, It also extends the application range of the compatibility relation-based methods for data mining.
21. The authors use the automatic indexing technique and the data mining technology to create a practical knowledge base, which can be used to extract information from three kinds of data on the Internet.
22. Extension knowledge obtainment is explored through two theorems of extension data mining and an example, and then the application of extension knowledge reasoning is explained.
23. The genetic algorithm plays an important role in area of data mining.
24. Himalaya Tools is a suite of programs focusing on new techniques in data mining.
25. Discovering association rules is one of more important tasks in data mining. One of the important problems is the evaluation of interestingness for the discovered rules.
26. The mining of uncertain knowledge is important problem in the field of machine learning and data mining.
27. In allusion to the equivalence relation and priority relation of the condition attributes, the method of data mining of rough set is analysized to treat with the attributes with priority.
28. There are three common personalized recommendatory technologies: information retrieval and extractor, content-based filtering and collaborative filtering, data mining and knowledge discovery.
29. Spatial clustering analysis is important method and study content of spatial analysis and spatial data mining.
30. Spatial clustering is one of the important research topic in spatial data mining, it is widely applied in spatial analysis.
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