data mining造句
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.
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.
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