Describe the bug I am trying to apply HDBSCAN to a dataset in order to find clusters with a certain maximum size (e.g. 5), but the max_cluster_size parameter is not working (i.e. the result contains clusters bigger than 5). As an example...
To make this concrete we need a notion of minimum cluster size which we take as a parameter to HDBSCAN. Once we have a value for minimum cluster size we can now walk through the hierarchy and at each split ask if one of the new clusters created by the split has fewer points than the...
DBSCAN（Density-Based Spatial Clustering of Applications with Noise，具有噪声的基于密度的聚类方法）是一种基于密度的空间聚类算法。该算法将具有足够密度的区域划分为簇，并在具有噪声的空间数据库中发现任意形状的簇，它将簇定义为密度相连的点的最大集合。 在DBSCAN算法中将数据点分为三类：
soft_assignments = np.argmax(soft_clusters, axis=1)# trained_classifier = hdbscan.HDBSCAN(prediction_data=True,# min_cluster_size=round(umap_embeddings.shape[0] * 0.007), # just < 1%/cluster# **hdbscan_params).fit(umap_embeddings)# assignments = best_clf.labels_logging.info('Done predicti...
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var result = HdbscanRunner.Run(new HdbscanParameters { DataSet = dataset, // double[][] for normal matrix or Dictionary<int, int>[] for sparse matrix MinPoints = 25, MinClusterSize = 25, CacheDistance = true, // use caching for distance MaxDegreeOfParallelism = 1, // to indicate all...
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cdef np.intp_t cluster_size cdef np.intp_t n result = np.empty(len(clusters), dtype=np.double) for n, c in enumerate(clusters): if np.isinf(max_lambda): cluster_size = np.sum(labels == n) if np.isinf(max_lambda) or max_lambda == 0.0 or cluster_size == 0: result[n] =...
I agree with daniel that ifcluster_selection_epsilonis a very large number (larger than [1 / root's lambda value]) HDBSCAN should return all points as the same label. However, currently it keeps all child_size==1 leaves of the root node as noise. ...
Then, the HDBSCAN algorithm was applied to the root points detected by GPR to perform an automatic cluster analysis, which determined the ZOIs of different shrubs. Finally, detailed information of each ZOI, such as its size and shape, was extracted and compared. The results of clustering roots...