步骤4: 实现 Single Pass 聚类 defsingle_pass_clustering(tfidf_matrix,threshold=0.5):clusters=[]# 存储聚类结果foriinrange(tfidf_matrix.shape[0]):current_doc=tfidf_matrix[i]found_cluster=Falseforclusterinclusters:# 使用余弦相似度计算当前文档与聚类文档的相似度sim=cosine_similarity(current_doc,cluste...
Gradual model generator for single-pass clustering - Karkkainen, Franti - 2005 () Citation Context ...ing operation is done by clustering all the weighted centroids in the ensemble using their weights. Weighted FCM (WFCM) is used for this purpose: We modified the objective function of FCM ...
# K-Means聚类 from sklearn.cluster import KMeans from time import time print("clustering keywords ...") t = time() n_clusters = 12 kmean = KMeans(n_clusters=n_clusters, max_iter=300, tol=0.0001, verbose=1, n_init=1000) kmean.fit(key_words_vec_array) print("kmean: k={}, ...
clustering = OnePassCluster(vector_list=temperature_all_city, t=9) clustering.print_result(label_dict=zone_dict) print(temperature_all_city) # 将聚类结果导出图 fig, ax = pl.subplots() fig = zone_dict c_map = pl.get_cmap('jet', clustering.cluster_num) c = 0 for cluster in clustering...
Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any complicated data compression techniques, ...
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fire Clustering makes minimal prior assumptions...
Single-cell transcriptional data can be represented by hierarchical clustering with each cell, represented as a column, and each gene, represented as a row in the heat map. There data can further be clustered using k-means clustering to reveal subsets that are altered in disease. Cells in clus...
Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights...
We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.Similar content being viewed by others RA3 is a reference-guided approach for epigenetic characterization of ...
While uncorrected data cluster by batch and not by cell type, DISCERN-integrated data show good batch mixing and clustering of cells by cell type across all five datasets (Fig. 1B and Additional file 1: Fig. S2). To get a clearer picture of DISCERN’s expression reconstruction capabilities,...