CURE算法是针对大规模数据聚类算法的典型代表。
CURE is a typical clustering algorithm that is designed for the mining of mass data.
同时，本文对流数据聚类算法的研究，对于促进同类问题的研究具有一定的理论价值和借鉴意义。
Meanwhile, the research of the stream data clustering algorithm would be useful references to the similar researches.
这种算法利用了数据挖掘中的聚类技术，可用于常规雷达和特殊雷达的信号分选。
The algorithm makes use of the clustering technology of data mining, can apply to general radar and special radar.
该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。
It can handle spatial data and spot any-shape clusters in a noised spatial database by dividing them into clusters with high enough density.
从多方面分析了该算法的性能，并将该算法应用于酵母细胞周期的芯片表达谱数据聚类。
The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data.
本文提出了一种有效的支持海量图像数据库Q BE查询的聚类索引算法。
This paper proposes an indexing algorithm of clustering which supports QBE image retrieval for large image databases.
聚类是一种把整个数据库分成不同的群组，使群与群之间差别很明显，而同一个群之间的数据尽量相似的算法。
Cluster is an algorithm, which can divide the data in the database into different groups, and there are obvious distinctions among groups.
聚类分析是数据挖掘的一个重要研究方向，而PAM算法是聚类算法中一个重要的方法。
Cluster is an important research direction and the PAM algorithm is one of the most important method.
针对异类传感器观测空间不一致的问题，提出了基于模糊聚类的异类多传感器数据关联算法。
For the inconsistency problem of heterogeneous sensors' measurement Spaces, a new data association (da) algorithm based on fuzzy clustering algorithm is presented.
聚类算法是数据挖掘的核心技术。
提出了一种基于密度网格的数据流聚类算法。
This paper introduced a density grid-based data stream clustering algorithm.
在公开数据集和人工数据集上的实验结果表明，DP算法能快速高效地找到接近于真实聚类中心的数据点作为初始聚类中心。
Experiments on both public and real datasets show that DP is helpful to find cluster centers near to real centers quickly and effectively.
聚类算法是数据挖掘领域中非常重要的技术。
Cluster arithmetic is a very important technology in the area of data mining.
本文介绍了地学空间数据迭代聚类的算法原理。
This paper presents algorithmic principles for approaching clustering of geo-spatial data.
本文介绍了数据挖掘理论，对聚类及孤立点检测算法进行了深入地分析研究。
In this thesis, the author presents the theory of data mining, and deeply analyzes the algorithms of clustering and outliers detection.
同时本篇论文也主要提出了一些经常被使用的数据挖掘的算法像聚类挖掘、关联规则挖掘、序列模式挖掘等。
Also, some of data mining algorithms that are commonly used in Web Usage mining are clustering, association rule generation, sequential pattern generation etc.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
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。
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.
数据流具有数据量无限且流速快等特点，使得传统的聚类算法不能直接应用于数据流聚类问题。
Data stream is characterized by infinite data and quick stream speed, so traditional clustering algorithm cannot be applied to data stream clustering directly.
基于网格的多密度聚类算法不仅能够对数据集进行正确的聚类，同时还能有效的进行孤立点检测，有效的解决了传统多密度聚类算法中不能有效识别孤立点和噪声的缺陷。
GDD algorithm can not only clusters correctly but find outliers in the dataset, and it effectively solves the problem that traditional grid algorithms can cluster only or find outliers only.
目前已经提出了许多数据流聚类算法，但是都尚未解决以上数据流环境下的要求。
While a lot of clustering algorithms for data streams have been proposed, they offer no solution to the combination of these requirements.
通过对离群数据来源及特性进行分析，定义了离群贡献度的概念，提出了一种基于特征赋权的离群数据再聚类算法。
By analyzing the origin and feature of outliers, a concept of exceptional contribution degree is defined and then an algorithm for re-clustering outliers based on feature weighting is proposed.
传统的基于网格的数据流聚类算法采用固定划分网格的方法，虽然算法的处理速度较快，但是聚类准确性较低。
A kind of traditional data cluster algorithm based on grid used the method of the fixed network division, with its faster processing but low accuracy.
聚类算法是数据挖掘算法中的重要解决方法。
Clustering algorithm is an important one in data mining methods.
现有的半监督聚类方法较少利用数据集空间结构信息，限制了聚类算法的性能。
Most of the existing semi-supervised clustering methods neglect the structural information of the data, while the few constraints available may degrade the performance of the algorithms.
并具体分析比较了多种的典型聚类和决策树数据挖掘算法。
Some classical clustering algorithms and decision trees algorithms are analyzed and compared.
实验表明，该算法对于解决数据流聚类问题非常有效。
Experimental results show that the algorithm is very effective to solve data stream clustering.
为了提高聚类效率提出了一种基于分布式的大数据集聚类算法。
In order to improve the efficiency we propose a distributed clustering algorithm based on large data sets.
标准的FCM算法对大数据样本集进行聚类时极为耗时，而且对噪声比较敏感。
The standard FCM algorithm is not only extremely time-consuming for clustering large data set, but also more sensitive to noise.
标准的FCM算法对大数据样本集进行聚类时极为耗时，而且对噪声比较敏感。
The standard FCM algorithm is not only extremely time-consuming for clustering large data set, but also more sensitive to noise.
应用推荐