Example for Agglomerative Clustering-优快云博客

本文详细介绍了层次聚类算法的基本概念、实现方法及在实际数据集中的应用案例，包括距离度量、聚类树构建和不同聚类策略的影响。通过实例分析，展示了层次聚类在数据分类和聚类分析领域的强大能力。

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转自http://en.wikipedia.org/wiki/Hierarchical_clustering

For example, suppose this data is to be clustered, and the Euclidean distance is the distance metric.

Cutting the tree at a given height will give a partitioning clustering at a selected precision. In this example, cutting after the second row will yield clusters {a} {b c} {d e} {f}. Cutting after the third row will yield clusters {a} {b c} {d e f}, which is a coarser clustering, with a smaller number of larger clusters.

Raw data

The hierarchical clustering dendrogram would be as such:

Traditional representation

This method builds the hierarchy from the individual elements by progressively merging clusters. In our example, we have six elements {a} {b} {c} {d} {e} and {f}. The first step is to determine which elements to merge in a cluster. Usually, we want to take the two closest elements, according to the chosen distance.

Optionally, one can also construct a distance matrix at this stage, where the number in the i-th row j-th column is the distance between the i-th and j-th elements. Then, as clustering progresses, rows and columns are merged as the clusters are merged and the distances updated. This is a common way to implement this type of clustering, and has the benefit of caching distances between clusters. A simple agglomerative clustering algorithm is described in the single-linkage clustering page; it can easily be adapted to different types of linkage (see below).

Suppose we have merged the two closest elements b and c, we now have the following clusters {a}, {b, c}, {d}, {e} and {f}, and want to merge them further. To do that, we need to take the distance between {a} and {b c}, and therefore define the distance between two clusters. Usually the distance between two clusters $\mathcal{A}$ and $\mathcal{B}$ is one of the following:

The maximum distance between elements of each cluster (also called complete-linkage clustering):

$\max \{\, d(x,y) : x \in \mathcal{A},\, y \in \mathcal{B}\,\}.$

The minimum distance between elements of each cluster (also called single-linkage clustering):

$\min \{\, d(x,y) : x \in \mathcal{A},\, y \in \mathcal{B} \,\}.$

The mean distance between elements of each cluster (also called average linkage clustering, used e.g. in UPGMA):

${1 \over {|\mathcal{A}|\cdot|\mathcal{B}|}}\sum_{x \in \mathcal{A}}\sum_{ y \in \mathcal{B}} d(x,y).$

The sum of all intra-cluster variance.
The increase in variance for the cluster being merged (Ward's method^[6])
The probability that candidate clusters spawn from the same distribution function (V-linkage).

Each agglomeration occurs at a greater distance between clusters than the previous agglomeration, and one can decide to stop clustering either when the clusters are too far apart to be merged (distance criterion) or when there is a sufficiently small number of clusters (number criterion).