sklearn spectralclustering
时间: 2023-05-01 10:03:07 浏览: 107
sklearn spectralclustering是一个Python中的机器学习库,用于进行谱聚类分析。谱聚类是一种基于图论的数据聚类方法,它将数据样本表示成图上的节点,通过计算节点之间的相似度及其对应的相似度矩阵,将节点划分成不同的聚类。sklearn spectralclustering库是基于这种方法实现的,可以方便地对数据进行聚类分析。
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SKlearn.clustering.spectralClustering
Spectral clustering is a clustering technique that uses the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The SpectralClustering class in the scikit-learn library is an implementation of this technique.
The SpectralClustering class takes the following parameters:
- n_clusters: the number of clusters to form
- affinity: the affinity matrix to use, which can be one of ‘nearest_neighbors’, ‘rbf’, or ‘precomputed’
- gamma: kernel coefficient for rbf kernel
- eigen_solver: the eigenvalue decomposition strategy to use, which can be one of ‘arpack’, ‘lobpcg’, or ‘amg’
- n_components: the number of eigenvectors to use when performing dimensionality reduction
Once the SpectralClustering instance is created, the fit_predict() method can be used to perform clustering on the data and return the cluster labels for each data point.
Spectral clustering can be useful for datasets with complex geometric structures or non-linear relationships between the data points. However, it can be computationally expensive for large datasets.
sklearn.cluster.spectralclustering
sklearn.cluster.spectralclustering 是 Python 中用于谱聚类的库。它可以将数据聚成预定数量的集群,并且能够处理非线性的数据或者不规则的数据形状,是一种强大的聚类算法。
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