Clustering effect evaluation problem in clustering algorithm

The clustering effect evaluation problem in the clustering algorithm requires specific code examples

Clustering is an unsupervised learning method that clusters data. Group similar samples into one category. In clustering algorithms, how to evaluate the effect of clustering is an important issue. This article will introduce several commonly used clustering effect evaluation indicators and give corresponding code examples.

1. Clustering effect evaluation index

1. Silhouette Coefficient

The silhouette coefficient is calculated by calculating the closeness of the sample and its relationship with other clusters The degree of separation is an indicator to evaluate the clustering effect. The value range of the silhouette coefficient is between [-1, 1]. The closer to 1, the better the clustering effect, and the closer to -1, the worse the clustering effect.

The following is a code example using Python to implement the silhouette coefficient:

from sklearn.metrics import silhouette_score

# 计算轮廓系数
silhouette_avg = silhouette_score(data, labels)
print("轮廓系数: %.4f" % silhouette_avg)

2. Calinski-Harabasz indicator (CH indicator)

Calinski-Harabasz indicator is calculated by The ratio of inter-class dispersion and intra-class dispersion is used to evaluate the clustering effect. The value range of the CH index is [0, ∞), and the larger the value, the better the clustering effect.

The following is a code example using Python to implement the CH indicator:

from sklearn.metrics import calinski_harabasz_score

# 计算CH指标
ch_score = calinski_harabasz_score(data, labels)
print("CH指标: %.4f" % ch_score)

3. Dunn indicator

Dunn indicator calculates the distance between nearest neighbor classes and the farthest The ratio of distances within neighboring classes is used to evaluate the clustering effect. The value range of Dunn's index is [0, ∞), and the larger it is, the better the clustering effect is.

The following is a code example using Python to implement the Dunn indicator:

from sklearn.metrics import pairwise_distances
import numpy as np

# 计算最近邻类间距离
def nearest_cluster_distance(clusters):
    min_distance = np.inf
    for i in range(len(clusters)):
        for j in range(i+1, len(clusters)):
            distance = pairwise_distances(clusters[i], clusters[j]).min()
            if distance < min_distance:
                min_distance = distance
    return min_distance

# 计算最远邻类内距离
def farthest_cluster_distance(clusters):
    max_distance = 0
    for i in range(len(clusters)):
        distance = pairwise_distances(clusters[i]).max()
        if distance > max_distance:
            max_distance = distance
    return max_distance

# 计算Dunn指标
dunn = nearest_cluster_distance(clusters) / farthest_cluster_distance(clusters)
print("Dunn指标: %.4f" % dunn)

2. Code example description

In the above code example, data is the input data set, and labels is the aggregate Class results, clusters are the set of samples for each cluster.

In practical applications, different clustering effect evaluation indicators can be selected according to specific needs. The silhouette coefficient is suitable for various types of data sets, the CH index is suitable for more balanced data sets, and the Dunn index is suitable for very unbalanced data sets.

By evaluating the clustering effect, better clustering algorithms and parameters can be selected to improve the accuracy and efficiency of cluster analysis.

Summary:

This article introduces the commonly used clustering effect evaluation indicators in clustering algorithms, including silhouette coefficient, CH indicator and Dunn indicator, and gives corresponding code examples. By evaluating the clustering effect, better clustering algorithms and parameters can be selected to improve the accuracy and efficiency of cluster analysis. In practical applications, appropriate indicators are selected for evaluation based on data characteristics and evaluation needs.

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