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   聚类数据挖掘 的翻译结果: 查询用时:0.208秒
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聚类数据挖掘
相关语句
  clustering data mining
     Genetic Algorithm Based Clustering Data Mining and Application in Sale System
     基于遗传算法的聚类数据挖掘及其在销售系统中的应用
短句来源
     Use Clustering Data Mining Technology to Providing Against Fraudulent Behavior in Telegraphy
     运用聚类数据挖掘技术预防电信业中的欺诈行为
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     Clustering Data Mining Applications in Department Store and K-means Clustering Algorithm Improvement
     聚类数据挖掘在商场中的应用及K-means聚类算法改进研究
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     Research on BOM Merger Based on Clustering Data Mining
     基于聚类数据挖掘技术的BOM归并关系研究
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     This theme put forward to a kind of customers clustering algorithm based on improving CABOSFV algorithm to solve how to cluster mass high-dimension square data which express customers behaviors after studying CRM and clustering Data Mining technology.
     本文通过对客户关系管理和聚类数据挖掘技术研究,提出基于改进的CABOSFV算法的客户聚类算法,用于解决客户关系管理中,对由大量高维稀疏数据组成的客户行为数据集进行聚类分析。
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  “聚类数据挖掘”译为未确定词的双语例句
     An Improved Conceptual Clustering Method for Data Mining
     一种改进的概念聚类数据挖掘方法
短句来源
     Facing the massive volume and high dimensional data how to build effective and scalable clustering algorithm for data mining is one of research directions of data mining.
     面对大规模的、高维的数据,如何建立有效、可扩展的的聚类数据挖掘算法是数据挖掘领域的一个研究热点。
短句来源
     Thus, data mining technology of clustering high-dimension square data is studied.
     所以,本文针对高属性维稀疏数据的聚类数据挖掘技术进行了研究。
短句来源
     Chapter two describes the method and criterion of clustering analysis, and appraises the clustering algorithms.
     第二章重点论述了聚类数据挖掘的方法及评价标准,对各类算法进行总结评价;
短句来源
     How to make the clustering faster and the result of the clustering more accurate is of the most importance and hardness.
     如何进行快速聚类以及如何取得更好的聚类结果成了聚类数据挖掘算法研究的重点和难点。
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  相似匹配句对
     Clustering Method in the Field of Data Mining
     数据挖掘聚类方法
短句来源
     Spatial Clustering Algorithm in Data Mining
     数据挖掘空间聚类
短句来源
     Clustering Method in Data Mining
     数据挖掘中的聚类方法
短句来源
     A Superfical Analysis about Clustering Used in Data Minin
     数据挖掘中的聚类浅析
短句来源
     The clustering data-mining on classification of DNA sequence
     DNA序列数据聚类挖掘
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Clustering is a promising application area for many fields including data mining, statistical data analysis, pattern recognition, image processing, etc. In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Unlike DBSCAN, the new DBSCAN uses only a small number of representative objects in a core object's neighborhood as seeds to expand the cluster so that the execution frequency of region query can be decreased, and consequently the...

Clustering is a promising application area for many fields including data mining, statistical data analysis, pattern recognition, image processing, etc. In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Unlike DBSCAN, the new DBSCAN uses only a small number of representative objects in a core object's neighborhood as seeds to expand the cluster so that the execution frequency of region query can be decreased, and consequently the I/O cost is reduced. Experimental results show that the new algorithm is effective and efficient in clustering large scale databases, and it is faster than the original DBSCAN by several times.

聚类是数据挖掘领域中的一个重要研究方向 .聚类技术在统计数据分析、模式识别、图像处理等领域有广泛应用 .迄今为止人们提出了许多用于大规模数据库的聚类算法 .基于密度的聚类算法 DBSCAN就是一个典型代表 .以 DBSCAN为基础 ,提出了一种基于密度的快速聚类算法 .新算法以核心对象邻域中所有对象的代表对象为种子对象来扩展类 ,从而减少区域查询次数 ,降低 I/ O开销 ,实现快速聚类 .对二维空间数据测试表明 :快速算法能够有效地对大规模数据库进行聚类 ,速度上数倍于已有 DBSCAN算法 .

Clustering, a useful technique for data mining, has promising applicati on perspective in many fields, such as pattern recognition, image processing and data compression etc. DBSCAN is a density-based clustering algorithm that can d iscover clusters of arbitrary shape and effectively handle noise, and requires o nly one input parameter. However, while dealing with large-scale databases, DBS C AN requires a lot of memory and can incur high I/O overhead. In this paper, we p resent a sampling-based DBSCAN algorithm(SDBSCAN)for...

Clustering, a useful technique for data mining, has promising applicati on perspective in many fields, such as pattern recognition, image processing and data compression etc. DBSCAN is a density-based clustering algorithm that can d iscover clusters of arbitrary shape and effectively handle noise, and requires o nly one input parameter. However, while dealing with large-scale databases, DBS C AN requires a lot of memory and can incur high I/O overhead. In this paper, we p resent a sampling-based DBSCAN algorithm(SDBSCAN)for clustering large-scale da tabases. Experimental results demonstrate that our algorithm is feasible and eff ective.

聚类是数据挖掘领域中的一个重要研究课题 .聚类技术在许多领域有着广泛的应用 .基于密度的聚类算法DBSCAN是一种有效的空间聚类算法 ,它能够发现任意形状的类并且有效地处理噪声 ,用户只需输入一个参数就可以进行聚类分析 .但是 ,DBSCAN算法在对大规模空间数据库进行聚类分析时需要较大的内存支持和 I/ O消耗 .本文在分析 DBSCAN算法不足的基础上 ,提出一种基于数据取样的 DBSCAN算法 ,使之能够有效地处理大规模空间数据库 .二维空间数据测试结果表明本文算法是可行、有效的 .

Clustering is widely studied in data mining community. It is used to partition data set into clusters so that intra-cluster data are similar and inter-cluster data are dissimilar. Different clustering methods use different similarity definition and techniques. Several popular clustering algorithms are analyzed from three different viewpoints: (1) clustering criteria, (2) cluster representation, and (3) algorithm framework. Furthermore, some new built algorithms, which mix or generalize some other algorithms,...

Clustering is widely studied in data mining community. It is used to partition data set into clusters so that intra-cluster data are similar and inter-cluster data are dissimilar. Different clustering methods use different similarity definition and techniques. Several popular clustering algorithms are analyzed from three different viewpoints: (1) clustering criteria, (2) cluster representation, and (3) algorithm framework. Furthermore, some new built algorithms, which mix or generalize some other algorithms, are introduced. Since the analysis is from several viewpoints, it can cover and distinguish most of the existing algorithms. It is the basis of the research of self-tuning algorithm and clustering benchmark.

聚类是数据挖掘中研究的重要问题之一.聚类分析就是把数据集分成簇,以使得簇内数据尽量相似,簇间数据尽量不同.不同的聚类方法采用不同的相似测度和技术.从以下3个角度分析现有流行聚类算法: (1)聚类尺度; (2)算法框架; (3)簇的表示.在此基础上,分析了一些综合或概括了一些其他方法的算法.由于分析从3个角度进行,所提出的方法能够涵盖,并区分绝大多数现有聚类算法.所做的工作是自调节聚类方法以及聚类基准测试研究的基础.

 
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