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.

17. try its best to gather and make good sentences.

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.

31. Explored the field of data mining in the supermarket business in a variety of applications, designed for the supermarket chain data mining program, and its sales analysis was studied.

32. The goal of data mining is to find the correct, novel, valuable and interpretable pattern from the mass data.

33. Mining association rules with ontological information bases on built domain ontology, using an algorithm of data mining, to produce semantic association rules which are more greatly satisfying users.

34. Classification is an important sub-branch of Data Mining, which can find out a model describing a predetermined set of data classes or concepts as used to predict the class label for a test sample.

35. In the emerging standard PMML(Predictive Model Markup Language), which is the platform and system independent representation of data mining models, there is no definition of the SOM meta-model.

36. The paper briefly introduced the concept of privacy preserving data mining technology and studied the application of decision tree classifier in this particular field.

37. The challenge is to make sure data mining doesn't become data strip mining - that we don't burn down the forest to make a lot of money quick but with no long term value.

38. I would like to focus on bio - informatics, database and data mining technology.

39. The technique of data mining is a new type of information processing technique.

40. The aim was to compare and classify these literatures and put forward reference for scholars who research development, new techniques and trends of time series data mining.

41. Binary entity relationship tuples can be applied in many fields such as knowledge base construction, data mining and pattern extraction and so on.

42. An effective data mining and forecasting method based on GA - NN in network management databases is provided.

43. As a jumped-up subject full of innovation, data mining has a broad application in many research fields, especially in chemo-metrology.

44. Based on fuzzy rough set ( FRS ) theory, an information decision system is built, in which some problems in the process of system building are coped with by data mining technology.

45. It is important that the data mining of multistage process should be study in the each phase.

46. The association analysis using data mining theory, a certain life insurance policies the company's history database of the excavation, has been reasonable, reliable association rules.

47. After discussing the control field format of microcommand in computer execution unit, an efficient algorithms is put forth. Then this algorithm is optimized through the search strategy of data mining.

47. is a sentence dictionary, on which you can find good sentences for a large number of words.

48. The application of data mining classification techniques in credit scoring involves the process of building and selecting optimal models.

49. The large-scale database or data warehouse is a huge amount of data in the real world, so data pre-processing must be done by attribute selection technology in Data Mining.

50. This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data.

51. Table Analysis Tools for Excel: This add-in provides easy-to-use tools that leverage SQL Server 2008 data mining features to perform powerful analytics on your spreadsheet data.

52. In the paper, based on hyper graph theory, a hypergraph model is proposed, which is useful for spatial data mining .

53. The combination of data mining theory and the index can help to make a scientific strategy for training and scientific achievement analysis.

54. First the paper introduces text categorization in the application of machine learning, pattern discrimination and data mining and explores the connection between text categorization and them.

55. The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts.

56. 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.

57. The novel commercial data mining system presented in this paper include three parts: data preparation, data preprocessing, mining and evaluation.

58. Data mining can combine top-down analysis techniques with bottom-up analysis techniques.

59. This paper is concern about the data mining research in the safe structure operation of the large port machine.

60. This paper mainly studies the technology of data mining for compressed data, including association rule mining, classification and clustering algorithm.

61. The thesis introduces concepts and present research status about data mining, semi-structured data mining and XML, and produces an oriented-XML treelike object model named TOM.

62. The Rough Set Theory can handle such problems as data reduction, data mining, the evaluation of attribute importance, the formation of decision algorithm etc.

63. After that incremental data mining algorithm of establishing decision matrix to each of decision type is put forward and the characteristic of rationality and validity are examined by example.

64. The implementation procedure can be extended to establish the data mining system for general industrial process.

65. Aiming at the application of data mining technology on forward business data, the forward prices and customers' behaviors are analyzed based on association rules.

66. Mining the association rules is an important aspect of the study of data mining, and one of the important problems is the evaluation of interestingness of the discovered rules.

67. This paper presents a method for assessment of loess collapsibility using the data mining technology.

68. To analyze each object independently is the base of the video data mining.

69. This paper introduces the process of data preparing processing of population information system data mining, including the regulation of population system, data clearing, data converting and ect.

70. Then uncertain knowledge presentation in the knowledge database and data mining method based on cloud model are given.

71. KEYW ORDS data mining, medical information system , decision support.

72. Decision tree is a basic learning method in machine learning and data mining.

73. A model of data mining is set up after preparation of data by means of attribute structure, and association rule algorithms are carried out. the data mining result is explained and analysed.

74. It constitute problem definition, data preparation , data raining and result analysis, problem definition and data preparation are very important in a data mining system.

75. According to quantitative parameters mapping relation, heuristic knowledge acquisition strategy based on qualitative reasoning and data mining is proposed.

76. The author takes CRISP - DM as the referenced model of the data mining process.

77. Part 3 will bring the " Data mining with WEKA" series to a close by finishing up our discussion of models with the nearest-neighbor model.

77. is a online sentence dictionary, on which you can find good sentences for a large number of words.

78. This text main research faces city Intellectual Traffic System's data analysis of spatiotemporal data mining.

79. Classification is one of the most basic tasks during the biological statistics and data mining.

80. Incremental updating algorithm and parallel processing technology were used to raise the efficiency of data mining and reduce the time complexity.

81. In KDD(Knowledge Discovery in Database), there are a number of patterns discovered from large database by data mining, but most of them are of no interestingness to the user.

82. Spatial clustering is the most fundmental task of spatial data mining. In this paper, uncertainties in spatial data mining are analyzed firstly.

83. With the development and amalgamation of data mining, artificial intelligence and mathematics statistics, Data mining comes into being.

84. OLAP and its implement are adopted in the data mining of radar clutter data warehouse.

85. Data mining based on Web Log is a main aspect of Web mining.

86. Weimann has argued that data mining could sniff out jihadists or remove information before would-be terrorists see it.

87. The incremental data mining function is implemented in order to making the whole system facing the decision users considering the system's entirety, initiative and efficiency.

88. In this paper , we have discussed the relations between data mining and reliability statistics.

89. Very eye-catcher of data mining the latest knowledge and I hope we can like!

90. Results The technology of data mining can be made the dynamic index of ration, picked up the analysis feedback speed, as well as made promptly the analysis and recalls.

91. Active incremental data mining is implemented by applying the trigger mechanism of the database to invoke procedures.

92. Secondly, this thesis studies the email data mining technology especially the email classification method.

93. Its about the analysis of data mining application under educational information condition, hope to offer value.

94. In this paper, a intelligent analysis mode based on Data Mining in Armored Equipment Maintenance and Management is presented to get a useful knowledge for the decisionmaker.

95. Using a relatively common algorithm CRD method as its basis, the kit can be divided into three main models which are data preprocessing, data mining and data evaluation.

96. Temporal data mining has the capability to discover patterns or rules which might be overlooked when the temporal component is ignored or treated as a simple numeric attribute.

97. In the previous two articles in this " Data mining with WEKA" series, I introduced the concept of data mining.

98. High - dimensional data mining faces the challenges of distributed data sparsity and overlapping feature subspace.

99. We study the incremental data mining technology based outlier factor.

100. After discussing the control field format of microcommand in computer execution unit, an efficient algorithm is put forth. Then this algorithm is optimized through the search strategy of data mining.

101. It is thought that the data mining is the multistage process of user' s center in this thesis.

102. The spatial clustering analysis is a method of the spatial data mining. The spatial clustering analysis can directly find some useful clustering structure from spatial database.

103. Clustering analysis is important part of data mining. It is an unsupervised learning process and it doesn't need prior knowledge about data set.

104. This paper builds a Bayesian inference network model based on the Rough Sets and Reason Rules and apply it to fulfill the medical data mining work.

105. This article wraps up the three-article series introducing you to the concepts of data mining and especially to the WEKA software.

106. All in all, a great gulf fixed request to exploit the tools of data mining, and convert the data " grave" to knowledge "gold ".

107. Now, Rough Set theory is becoming a new hotspot of artificial intelligence domain. More and more scholar focuse on the incremental data mining technology based on rough set theory.

108. However, the datasets which data mining search always contain missing data.

109. This paper also studies the method of marketing management decision-making model by data mining technique.

110. And then, an approach for knowledge reduction is given in the construction of history knowledge base to store interesting rules in incremental data mining.

111. Data -reduction-based approximate data mining technique in which data reduction for massive data set was done in data pretreatment phase has been discussed.

112. To mine the association rules with multi dimensional numeric attribute is a difficult problem in data mining area.

113. It has been developed with the objective of serving as an annotated and curated database comprising complete genome sequences of viruses, value-added derived data and data mining tools.

114. There are two types of data mining methods integration, they are horizontal integration and vertical integration.

115. The paper briefly introduces the concept of privacy preserving data mining technology and studies the application of decision tree classifier in this particular field.

116. Spatial Data Mining is a research branch of data mining, and the spatial clustering analysis is an important area of research of spatial data mining.

117. Our proposed CAAR algorithm is applied to supervised classification of image content and large-scale data mining, which is very effective.

118. Depending on all above studies and experiments, we can conclude that using data mining distill prosodic rules in speech synthesis is viable.

119. Through actual validating, the results indicate that this application used in telecom system based on data mining is more effective.

120. Clustering analysis is one of the important data mining techniques that can discover hidden modes by unsupervised learning and has the ability of acquiring knowledge independently.

121.

122.

123.

124.

125.

126.

127.

128.

129.

130.

131.

132.

133.

134.

135.

136.

137.

137. try its best to collect and make good sentences.

138.

139.

140.

141.

142.

143.

144.

145.

146.

147.

148.

149.

150.

151.

152.

153.

154.

155.

156.

157.

158.

159.

160.