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1·For the lack of valuable prior knowledge in the image retrieval process, unsupervised clustering algorithms should be applied.

因为在对图像进行聚类分析时，通常缺少可资利用的先验知识，所以需要采用无监督的聚类算法。

2·Compared to unsupervised clustering, semi-supervised clustering utilizes a small amount of given prior knowledge to guide the clustering process.

相比于无监督聚类分析，半监督聚类利用提供的少量监督信息协助指导聚类过程。

3·The data of IRIS indicates that the algorithm possesses the better convergence, better robustness and it is an unsupervised clustering algorithm.

IRIS数据检验表明，未确知均值聚类算法误判样本数少、收敛速度快、鲁棒性好，是一种实用、有效的无监督聚类算法。

4·This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the 'image, and finish the automatic segmentation.

这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数，从而完成对图像的自动分割。

5·Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.

无监管学习的常见方法包括k - Means、分层集群和自组织地图。

6·We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.

提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。

7·Because scenes have much difference in appearance, we adopt unsupervised scene clustering for its universality, which groups video shots with similar visual content into same cluster.

对于视频来说，由于存在较大的场景差异，因此采用无监督的场景聚类，达到通用性的目的。

8·Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.

现在我已经做了对的监督和无监督学习算法，如决策树，一些基本的阅读聚类，神经网络等。

9·After analysing manifold color clustering methods, we present two unsupervised methods based on 2-d chroma plane, i. e, the density-based triangulation method and the GA-based optimization method.

在分析了多种颜色聚类方法的基础上，提出了2种无监督的二维色度平面颜色聚类方法：基于密度的三角化方法和遗传算法优化方法，并通过实验对2种方法进行了比对分析。

10·Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.

研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。