Machine Learning 简明教程

Machine Learning - Clustering Algorithms

聚类方法是最有用的无监督 ML 方法之一。这些方法用于查找数据样本之间的相似性和关系模式，然后根据特征将这些样本聚类到具有相似性的组中。聚类很重要，因为它确定了当前未标记数据中的内在分组。它们基本上对数据点做出一些假设以构成它们的相似性。每个假设都会构建不同但同样有效的聚类。

Clustering methods are one of the most useful unsupervised ML methods. These methods used to find similarity as well as relationship patterns among data samples and then cluster those samples into groups having similarity based on features. Clustering is important because it determines the intrinsic grouping among the present unlabeled data. They basically make some assumptions about data points to constitute their similarity. Each assumption will construct different but equally valid clusters.

例如，以下是显示聚类系统将不同聚类中相似类型的数据分组在一起的图表：

For example, below is the diagram which shows clustering system grouped together the similar kind of data in different clusters −

Cluster Formation Methods

聚类不必以球形形式形成。以下是一些其他聚类形成方法：

It is not necessary that clusters will be formed in spherical form. Followings are some other cluster formation methods −

Density-based − In these methods, the clusters are formed as the dense region. The advantage of these methods is that they have good accuracy as well as good ability to merge two clusters. Ex. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to identify Clustering structure (OPTICS) etc.
Hierarchical-based − In these methods, the clusters are formed as a tree type structure based on the hierarchy. They have two categories namely, Agglomerative (Bottom up approach) and Divisive (Top down approach). Ex. Clustering using Representatives (CURE), Balanced iterative Reducing Clustering using Hierarchies (BIRCH) etc.
Partitioning − In these methods, the clusters are formed by portioning the objects into k clusters. Number of clusters will be equal to the number of partitions. Ex. Kmeans, Clustering Large Applications based upon randomized Search (CLARANS).
Grid − In these methods, the clusters are formed as a grid like structure. The advantage of these methods is that all the clustering operation done on these grids are fast and independent of the number of data objects. Ex. Statistical Information Grid (STING), Clustering in Quest (CLIQUE).

聚类不必以球形形式形成。以下是一些其他聚类形成方法：

It is not necessary that clusters will be formed in spherical form. Followings are some other cluster formation methods −

Density-based

在这些方法中，聚类被形成为稠密区域。这些方法的优点在于，它们既具有良好的准确性，又有合并两个聚类的良好能力。例如，带噪声的基于密度的空间聚类应用 (DBSCAN)，用于识别聚类结构的排序点 (OPTICS) 等。

In these methods, the clusters are formed as the dense region. The advantage of these methods is that they have good accuracy as well as good ability to merge two clusters. Ex. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to identify Clustering structure (OPTICS) etc.

Hierarchical-based

在这些方法中，聚类被基于分层形成为树型结构。它们有两个类别，即凝聚（自底向上的方法）和分裂（自顶向下的方法）。例如，使用代表的聚类 (CURE)，使用层次结构的平衡迭代缩小聚类 (BIRCH) 等。

In these methods, the clusters are formed as a tree type structure based on the hierarchy. They have two categories namely, Agglomerative (Bottom up approach) and Divisive (Top down approach). Ex. Clustering using Representatives (CURE), Balanced iterative Reducing Clustering using Hierarchies (BIRCH) etc.

Partitioning

在这些方法中，聚类是由将各个对象分配到 k 个聚类中而形成的。聚类数将等于分区数。例如，K 均值，基于随机搜索聚类大型应用程序 (CLARANS)。

In these methods, the clusters are formed by portioning the objects into k clusters. Number of clusters will be equal to the number of partitions. Ex. K-means, Clustering Large Applications based upon randomized Search (CLARANS).

Grid

在这些方法中，聚类被形成为网格状结构。这些方法的优点在于，在这些网格上进行的所有聚类操作都很快，并且与数据对象的数量无关。例如，统计信息网格 (STING)，寻求聚类 (CLIQUE)。

In these methods, the clusters are formed as a grid like structure. The advantage of these methods is that all the clustering operation done on these grids are fast and independent of the number of data objects. Ex. Statistical Information Grid (STING), Clustering in Quest (CLIQUE).

Types of ML Clustering Algorithms

以下是最重要的有用的 ML 聚类算法 −

The following are the most important and useful ML clustering algorithms −

K-means Clustering

此聚类算法计算质心并迭代直至找到最佳质心。它假定已知聚类数。它也被称为平面聚类算法。算法从数据识别的聚类数在 K 均值中表示为“K”。

This clustering algorithm computes the centroids and iterates until we it finds optimal centroid. It assumes that the number of clusters are already known. It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by ‘K’ in K-means.

Mean-Shift Algorithm

这是无监督学习中使用的另一种强有力的聚类算法。与 K 均值聚类不同，它不作任何假设，因此它是一种非参数算法。

It is another powerful clustering algorithm used in unsupervised learning. Unlike K-means clustering, it does not make any assumptions hence it is a non-parametric algorithm.

Hierarchical Clustering

这是另一种无监督学习算法，用于对具有相似特征的未标记数据点进行分组。

It is another unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics.

我们将在接下来的章节中详细讨论所有这些算法。

We will be discussing all these algorithms in detail in the upcoming chapters.

Applications of Clustering

我们可以在以下领域发现聚类很有用 −

We can find clustering useful in the following areas −

Data summarization and compression − 聚类被广泛用于我们要求数据汇总、压缩和减少的领域。例如图像处理和矢量量化。

Data summarization and compression − Clustering is widely used in the areas where we require data summarization, compression and reduction as well. The examples are image processing and vector quantization.

Collaborative systems and customer segmentation − 由于聚类可以用于查找类似产品或同类用户，因此它可以用于协作系统和客户细分领域。

Collaborative systems and customer segmentation − Since clustering can be used to find similar products or same kind of users, it can be used in the area of collaborative systems and customer segmentation.

Serve as a key intermediate step for other data mining tasks − 聚类分析可以生成用于分类、测试、假设生成的数据的紧凑摘要；因此，它也作为其他数据挖掘任务的关键中间步骤。

Serve as a key intermediate step for other data mining tasks − Cluster analysis can generate a compact summary of data for classification, testing, hypothesis generation; hence, it serves as a key intermediate step for other data mining tasks also.

Trend detection in dynamic data − 通过创建具有类似趋势的不同聚类，聚类还可以用于动态数据中的趋势检测。

Trend detection in dynamic data − Clustering can also be used for trend detection in dynamic data by making various clusters of similar trends.

Social network analysis − 聚类可以用于社交网络分析。例如，在图像、视频或音频中生成序列。

Social network analysis − Clustering can be used in social network analysis. The examples are generating sequences in images, videos or audios.

Biological data analysis − 聚类还可以用于生成图像和视频聚类，因此可以成功地用于生物数据分析。

Biological data analysis − Clustering can also be used to make clusters of images, videos hence it can successfully be used in biological data analysis.

既然您了解聚类是什么以及它是如何工作的，那么让我们在接下来的几章中了解一些机器学习中使用的聚类算法。

Now that you know what is clustering and how it works, let’s see some of the clustering algorithms used in machine learning, in the next few chapters.