sklearn.cluster.spectralclustering
时间: 2023-05-02 15:00:36 浏览: 67
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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sklearn.cluster
Sklearn.cluster is a module in the scikit-learn library that provides various clustering algorithms. Clustering is a technique of grouping similar data points together in such a way that data points in the same group are more similar to each other than to those in other groups. There are many applications of clustering, such as market segmentation, image segmentation, and anomaly detection.
Some of the clustering algorithms provided by sklearn.cluster are:
1. KMeans: It is a popular clustering algorithm that partitions the data into K clusters.
2. AgglomerativeClustering: It is a hierarchical clustering algorithm that starts with each data point as a separate cluster and merges them iteratively based on a linkage criterion.
3. DBSCAN: It is a density-based clustering algorithm that groups together dense regions of data points separated by areas of lower density.
4. SpectralClustering: It is a clustering algorithm that uses graph theory to group together data points that are connected by edges in a graph.
5. Birch: It is a clustering algorithm that incrementally builds a hierarchical clustering tree to cluster the data points.
Sklearn.cluster also provides various metrics to evaluate the quality of the clustering results, such as silhouette score, homogeneity score, completeness score, and adjusted mutual information score.
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.