K-Medoid(PAM)算法的缺点 [英] Drawbacks of K-Medoid (PAM) Algorithm

问题描述

我研究了K-medoid算法(PAM)是基于分区的聚类算法，是K-means算法的一种变体.它解决了诸如产生空簇之类的K均值问题以及对离群值/噪声的敏感性.

I have researched that K-medoid Algorithm (PAM) is a parition-based clustering algorithm and a variant of K-means algorithm. It has solved the problems of K-means like producing empty clusters and the sensitivity to outliers/noise.

但是，与K-means(劳埃德算法)的时间复杂度为O(n)相比，K-medoid的时间复杂度为O(n ^ 2).我想问一下K-medoid算法除了时间复杂度之外是否还有其他缺点.

However, the time complexity of K-medoid is O(n^2), unlike K-means (Lloyd's Algorithm) which has a time complexity of O(n). I would like to ask if there are other drawbacks of K-medoid algorithm aside from its time complexity.

推荐答案

K-Medoid算法(PAM，CLARA或CLARANS)的主要缺点是它们不适用于对非球形(任意形状)组进行聚类对象. 这是因为它们依靠最小化非medoid对象和medoid(聚类中心)之间的距离-简而言之，他们使用紧凑性作为聚类标准而不是连通性.

The main disadvantage of K-Medoid algorithms (either PAM, CLARA or CLARANS) is that they are not suitable for clustering non-spherical (arbitrary shaped) groups of objects. This is because they rely on minimizing the distances between the non-medoid objects and the medoid (the cluster center) - briefly, they use compactness as clustering criteria instead of connectivity.

PAM的另一个缺点是，由于第一个 k 个类固醇是随机选择的，因此对于同一数据集的不同运行，PAM可能会获得不同的结果.

Another disadvantage of PAM is that it may obtain different results for different runs on the same dataset because the first k medoids are chosen randomly.

除了上述缺点外，还必须预先指定 k 的值(簇数).

In addition to the aforementioned disadvantages, you must also specify the value for k (the number of clusters) in advance.

这篇关于K-Medoid(PAM)算法的缺点的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！