data clustering造句

• Degree of data clustering with the index
  使用索引的情况下数据群集的程度
• Improved fuzzy kernal algorithm for gene expression data clustering
  基因表达数据聚类中模糊核算法的改进
• Cluster - pc based parallel distributed data clustering and its applications
  基于集群的并行分布式聚类及其应用
• Then a new data clustering method based on pcnn is also presented , which is prospective and has a wide application
  该方法构思新颖、适用范围广、具有广阔的应用前景。
• Association matrix between behavior and object is defined and subsystems are derived based on data clustering
  定义了行为和对象的关联度矩阵，基于聚类方法确定信息系统子系统的建设需求。
• It first introduces the concept of data clustering and data classification . then it discusses eleven methods of cluster analysis
  首先介绍聚类和分类的概念，然后讨论了十一种数据聚类方法。
• Furthermore we try to get a new approach by combining data clustering and outlier detection . discuss the sampling techniques frequently used in clustering and outlier detection
  对聚类利异常检测算法中大量使用的采样技术进行了讨论，并提山了密度偏向的采样技术。
• Next an index system including 8 indexes to measure position knowledge amount is constructed , by which we performed classification on those positions by the statistical methods of data clustering and factor analysis
  随后归纳出衡量一般岗位知识含量的体系的8个指标，用聚类和因子分析方法将各岗位按知识含量高低进行分级。
• In this paper , we do a lot of experiments on artificial data and real world data , and make a comparison with classical data clustering methods . the experimental results prove its great advantage over others
  本文中，我们将它与传统的数据聚类方法分别对模拟数据和真实数据做以实验，并加以比较，证明了该方法优于传统方法，验证了它的优良性。
• After that , the thesis proposes a algorithm to seek the degree of the texture of a digital image . 3rd . it gives a new model for data clustering , " village - town " model and a clustering globally best algorithm based on genetic method
  以待聚类数据集为对象从寻求全局最优配置入手，首次提出了基于遗传算法的聚类变精度搜索方案和基于点分布密度的类合并准则。
• Clustering analysis is one of most heated research topic of the day . data clustering , a unsupervised classifying method , is the process of grouping together similar multi - dimensional data vectors into a number of clusters or bins
  聚类就是把一个没有类别标记的样本集按某种准则划分成若干类，使类内样本的相似性尽可能大，而类间样本相似性尽量小，是一种无监督的分类方法。
• At the aspect of preprocess , some preprocess methods are studied and improved , including rough set , data clustering , concept hierarchies and language field , etc . at the aspect of mining algorithms , classification is an important knowledge discovery method
  在数据的预处理方面，主要研究粗集理论、数据聚类、概念树、语言场等预处理方法。在挖掘模型与算法的选取中，分类是一种重要的知识发现方法，它能以简洁的模型预测新到达对象的类别。
• ( 4 ) seven kinds of spatial data clustering approaches are studied . and the technique to solve the problem of constraint - based spatial cluster analysis is explored . in addition , a new spatial clustering algorithm based on genetic algorithms is set forward and it can give attention to local constringency and the whole constringency
  ( 4 )系统研究了七种典型的空间数据聚类方法，积极探索基于约束条件的空间聚类问题的解决方案；将遗传算法引入空间数据聚类领域，提出一种基于遗传算法的空间聚类算法，该算法兼顾了局部收敛和全局收敛性能。
• In this thesis , several methods such as wavelet analysis , fuzzy theory , data clustering , neural network , model recognition and genetic algorithm are mentioned . and the author has made some improvement in fields of digital image analysis and encryption with genetic algorithm and wavelet analysis
  在本文各章节中分别使用了小波分析、模糊数学、数据聚类、神经网络、模式识别和遗传算法等技术，并对遗传算法和小波分析在数字图像分析与加密的应用领域内分别有所拓展。
• What is data mining is first discussed , including the emergence background and definition of data mining . then some important subjects of data mining at home and abroad are introduced , such as association rules , data generalization , data classification , data clustering etc . finally , some challenges in the research and application of data mining are discussed , which contribute to the advanced development of data mining
  第一章首先介绍了什么是数据挖掘，包括数据挖掘的产生背景和定义，介绍了目前国内外数据挖掘中研究的一部分重要内容的概况，包括关联规则、数据综合和概括、数据分类、数据聚类等。
• Data clustering , an important branch of data mining , is the process of group the data into classes or cluster so that the objects within a cluster have high similarity in comparison to one another , but are very dissimilar to objects in other clusters . and it is helpful to search some schemes and data distributions , which are novel , effective , useful or understandable
  聚类是将数据点集合分成若干类或簇( cluster ) ，使得每个簇中的数据点之间最大程度地相似，而不同簇中的数据点最大程度地不同；从而发现人数掘集中有效的、新颖的、有用的利可以现解的模式利数据分布。
• This essay first dicussed the key steps of preprocessing in web log mining , which include data abstract , data cleaning , user and session identification and path completion etc . especialy we proposed the algorithm of the web log data preprocessing include frame page . and secondly we discussed the technology of building an adaptive web site , include log data cluster mining , user visiting pattern learning , site structure transformation and presentation etc . ; and we proposed indual user log visiting pattern , user model onling learning algorithm , index pages synthesising algorithm , site structure transformation and presentation algorithm and so on
  本论文首先讨论了web日志挖掘预处理中的各步骤：数据抽象、数据清洗、用户与会话识别、访问路径补全，给出了每一步骤的算法实现；并特别讨论了含有frame页的日志数据预处理过滤算法。其次讨论了构建自适应站点技术，包括日志数据聚类挖掘、用户访问模式学习、站点结构转化与呈现等；提出了单用户日志访问模型，给出了用户模型在线学习算法、索引页面综合算法、站点结构转化及呈现算法等。
• Based on the comparing analysis and character of clustering algorithm the simulated annealing （ sa ） algorithms was applied to the data clustering . simulated annealing （ sa ） algorithms are random search techniques based on physical annealing process , which can prevent the optimizing process into local optimization and get the global optimization
  算法以优化过程的求解与物理退火过程的相似性为基础，通过接受准则和对下降温度的控制，能够有效的克服优化过程陷入局部极小从而获得全局最优解。