查询词典 clustering

First, the character clustering algorithm regards the character as our features and then it can cluster these sentences of our target words to the correct group without any other resources. The average precision is 66.1%. Second, in the concept clustering algorithm of the aggregate computing, we use HowNet as the knowledge base of our feature words and obtain the concepts of these words. We cluster the sentences which have the same or similar concepts of the feature words into the same group. And then we can complement some lacks of the character clustering algorithm. The average precision is 72.3%. Third, regarding the concept clustering algorithm of the sememe distance, we use the sememe distance to compute its concept similarity. It improved similarity measure of the concept clustering algorithm of the aggregate computing. It achieves 81% average precision and gets better cluster quality.

词形分群演算法不受语料资源限制，能将词形相似且词义相近的词汇所属的句子分到同一群，经过人工验证，得到了66.1%的平均正确率；基於集合计算的概念分群演算法使用了知网做为撷取特徵词汇的知识库，透过知网取得词汇的概念，将具相同或相似概念的特徵词汇所属的句子分成同一群，补足词形分群演算法的不足，得到72.3%的平均正确率；基於义原距离的概念分群演算法则利用义原间的距离计算特徵概念的相似度，进一步改善了基於集合计算的概念分群演算法在相似度衡量的问题，得到81%的平均正确率，达到更好的分群效果。

Researches on kernel clustering algorithms. After combining K-means clustering algorithm and the theory of kernel-based learning algorithms, we propose a fast kernel K-means clustering method which is based on CPD kernel. The experiment results indicate that the clustering effect of the algorithm is better than that of K-means algorithm, the clustering speed of the algorithm is also fast than that of K-means algorithm.

核聚类算法的研究：探讨了K-均值聚类算法，通过将核学习理论与K-均值聚类算法结合，提出了一种基于CPD核函数的快速核K-均值聚类算法，并将该算法与基于Mercer核的核聚类算法进行了比较，实验结果显示，我们的方法不仅比K-均值聚类算法的聚类效果好，而且聚类速度快。

A new hybrid clustering algorithm in phase of high-efficiency and good quality was put forward on the foundation of synthetically analyzing K-means clustering algorithm based on partition and agglomerative clustering algorithm based on hierarchy, and consulting some improved hybrid clustering algorithms.

在综合分析基于划分的K均值聚类算法和基于层次的凝聚聚类算法的基础上，借鉴各种混合聚类方法，提出了一种执行效率更高和聚类质量更好的分阶段混合聚类算法。

A new hybrid clustering algorithm in phase of highefficiency and good quality was put forward on the foundation of synthetically analyzing Kmeans clustering algorithm based on partition and agglomerative clustering algorithm based on hierarchy, and consulting some improved hybrid clustering algorithms.

在综合分析基于划分的K均值聚类算法和基于层次的凝聚聚类算法的基础上，借鉴各种混合聚类方法，提出了一种执行效率更高和聚类质量更好的分阶段混合聚类算法。

For this reason, the old methods can not fally meet the need of engineering practical application.3 By analyzing the principle and expound the methods of thecand the gray relationship clustering, the gray clustering matrix and method are presented.The gray clustering is a method that gather some obseration quotas or obseration objects to be some definable classification according to the gray relationship clustering matrix or gray albescent authority function.

3分析了灰色白化权函数聚类和灰色关联聚类的原理并阐述了其具体方法，提出了诊断变压器内部故障的灰色聚类模型和方法，灰色聚类是根据灰色关联矩阵或灰色白化权函数将一些观测指标或观测对象聚集成若干个可定义类别的方法；对结构复杂的大型电力变压器等电气设备的故障诊断，首要的问题是如何根据反映变压器故障的特征量指标来正确判断待诊设备是哪一类故障。

For contrast experiments, k-means and DBSCAN algorithms are also implemented using Visual C++ 6. 0. We conducted a series of experiments, including the experiment of the correctness of clustering and outlier detection, the experiment of the precision of clustering and outlier detection. The experiment of the runtime, the experiment of the effect of clustering and outlier detection on parameters, the experiment of the impact of clustering and outlier detection precision by the order of data input, and the experiment of the effect of the algorithm validness by the density character of dataset.

本文使用Visual C++ 6.0实现了基于距离的聚类和孤立点检测算法、k-means算法和DBSCAN算法，做了大量的对比实验，包括聚类算法和孤立点检测正确性实验；聚类算法和孤立点检测精度实验；算法执行时间实验；参数对聚类和孤立点检测结果的影响实验；数据输入顺序对算法聚类和孤立点精度的影响实验；数据集密度对算法有效性的影响等。

Subsequently, clustering analysis in data mining is disserted, involving the methods and characteristics of clustering used in data mining and the methods for evaluating the clustering results, with emphasis on clustering the data with categorical attributes.

在此基础上对数挖掘中的聚类分析作以详细地论述，总结了数挖掘中聚类分析的方法和特点，并对聚类结果的评价方法进行了讨论，重点讨论了分类属性数据聚类，具体研究了k-modes 算法及其变形，并指出了它们的优缺点。

The definition of the clustering and the algorithms in the clustering is introduced. We introduce the present situation of the clustering in time series, and now there are two kinds of clustering algorithm in time series, one is Adaptive Resonance Theory and their improvement algorithms; the other is Self-Organizing Feature Map and their improvement algorithms.

对聚类分析的概念作了简要介绍，讨论了现有的聚类分析中常用的方法以及时间序列的聚类分析的一些算法，当前聚类用于时间序列的符号化主要有两类，第一类是基于竞争学习模型的方法及其改进算法，第二类是自组织特征映射及其改进算法，本文对这两类算法分别作了探讨。

The selection of starting center points of clustering has great effects on the constringency speed of this clustering algorithms and the performance of clustering.

聚类初始中心的选择对该聚类算法的收敛速度和聚类的性能都有很大的影响。

The main contribution of this thesis is that we propose a collaborative filtering recommendation algorithm based on double clustering. This approach first respectively clusters resources and users by the users rating on items, then makes a collaborative filtering recommendation based on the clustering result. The new algorithm can shorten on-line recommendation time. Then, we apply the classical formula of cosine correlation to double clustering algorithm, leaving out the standardization operation. In the end, we implement an educational resource recommender system according to the need of the actual project and the result is positive.

本文的主要意义在于，首先运用聚类技术对用户和资源分别进行聚类，然后利用聚类结果进行协同过滤推荐，由于聚类部分离线周期进行，大大缩短了在线的推荐时间；然后将经典的余弦相似性计算公式运用到双重聚类算法中，省去规范化处理操作，减少运算量；最后结合实际的需要，实现一个资源推荐系统。

More direct, directer ; most direct, directest

径直的，笔直的

Do you know how to use a chain saw?

你知道如何使用链锯吗？