登录查看更多内容

What are the best ways to measure clustering algorithm performance?

由人工智能和领英社区提供技术支持

Clustering is a technique that groups similar data points together based on some criteria, such as distance, density, or connectivity. Clustering algorithms are useful for exploratory data analysis, dimensionality reduction, anomaly detection, and more. But how do you know if your clustering algorithm is doing a good job? How do you compare different clustering methods or tune their parameters? In this article, you will learn about some of the best ways to measure clustering algorithm performance.

在这篇协作文章中查找专家回答

由社区从 2 条内容中精选。了解更多

1 Internal measures

Internal measures are used to evaluate the quality of a clustering result without using any external information or labels. Such measures focus on cohesion and separation, which is how tightly the data points are grouped within each cluster and how far apart the clusters are from each other. The Silhouette coefficient, for instance, measures the average similarity of each data point to its own cluster compared to the most similar other cluster, ranging from -1 to 1 (higher values indicate better clustering). On the other hand, the Davies-Bouldin index computes the average ratio of the within-cluster scatter and the between-cluster separation for each cluster, ranging from 0 to infinity (lower values indicate better clustering). Additionally, the Calinski-Harabasz index calculates the ratio of the between-cluster variance and the within-cluster variance, multiplied by a factor that depends on the number of clusters and data points; it ranges from 0 to infinity (higher values indicate better clustering).

添加您的观点

Ariel Goh

PhD | Software Engineer
举报内容
The Gap Statistic contrasts the within-cluster variation to a reference null distribution, introducing a probabilistic framework to the evaluation process. The Dunn index offers another dimension, emphasizing the distinction between clusters through the lens of minimal inter-cluster distances against maximal intra-cluster sizes, promoting the discovery of compact, clearly delineated groupings. Graph-based measures like the BetaCV and Normalized Cut evaluate clustering by balancing within-cluster similarity against between-cluster dissimilarity, thus aligning closely with the clustering objectives in networked data. One can integrate various internal measures to offer a composite of clustering efficacy.

已翻译

赞

2 External measures

External measures evaluate the quality of a clustering result based on external information or labels that are not used by the clustering algorithm. These measures compare the clusters to predefined classes or categories that represent the true or desired grouping of the data. The Rand index calculates the fraction of data point pairs that are correctly assigned to the same or different clusters according to the external labels, ranging from 0 to 1 with higher values indicating better clustering. The Adjusted Rand index adjusts the Rand index for chance agreement between clusters and labels by subtracting the expected value and dividing by the maximum value, ranging from -1 to 1 with higher values indicating better clustering. Lastly, the Normalized mutual information measure calculates mutual information between clusters and labels, normalized by the average of their entropies, ranging from 0 to 1 with higher values indicating better clustering. Reporting and documenting these external measures will help you inform and reassure stakeholders, as well as provide valuable insights for improving your cloud security posture.

添加您的观点

Ariel Goh

PhD | Software Engineer
举报内容
The Fowlkes-Mallows index measures the geometric mean of the precision and recall, thus requiring a pair of points to be simultaneously in the same cluster and class to be considered. This index ranges from 0 to 1, where 1 indicates perfect clustering. The Jaccard index considers only the positive agreements, offering a stringent assessment of clustering performance, ideal for imbalanced datasets where the prevalence of one class can skew results. The Homogeneity, Completeness, and V-measure separately assess whether clusters contain only data points which are members of a single class, whether all data points that are members of a given class are elements of the same cluster, and a harmonic mean of the two, respectively.

已翻译

赞

3 Validation methods

Validation methods are techniques that help you select the most suitable clustering algorithm, the optimal number of clusters, or the best parameter settings for your data. These methods evaluate internal or external measures of different clustering results and compare them. Elbow method plots the internal measure values against the number of clusters and looks for a point where the curve bends or flattens. Gap statistic compares the internal measure values of the actual data to those of some reference data generated randomly or uniformly, and identifies the number of clusters that maximizes the gap between them. Cross-validation splits the data into training and test sets, applies clustering algorithm to training set, and evaluates external measure values on test set. It then repeats this process for different splits and averages the results.

Measuring clustering algorithm performance is not a straightforward concept as different measures and methods may yield different results or preferences. It is important to understand assumptions and limitations of each measure and method, as well as use them in combination with domain knowledge and intuition. What challenges or tips have you encountered when measuring clustering algorithm performance?

添加您的观点

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Algorithms

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the best ways to measure clustering algorithm performance?

1

2

3

4

1 Internal measures

2 External measures

3 Validation methods

4 Here’s what else to consider

Algorithms

给文章评分

感谢您的反馈

更多Algorithms相关文章

更多相关阅读内容

What are the best ways to measure clustering algorithm performance?

1

2

3

4

1 Internal measures

2 External measures

3 Validation methods

4 Here’s what else to consider

Algorithms

给文章评分

感谢您的反馈

查看其他技能