我想我已经知道了我的答案，但那里有比我更聪明，更有经验的人，所以我想问。
我在试图让MemoryError（hash_matrix）符合<class 'scipy.sparse.csr.csr_matrix'>时遇到AffinityPropagation。它仅在10,000个样本上失败，这在我的实际数据集范围内相对较小。
我的问题：我喜欢AffinityPropagation在较小数据集上看到的结果，但除非我能够将其应用于更大的数据集，否则它无用。
我的问题：是否正在尝试将AffinityPropagation放在标准笔记本电脑上不太可能发生的数十万件物品上？
我学到了什么：
AffinityPropagation does not support partial_fit和增量学习。AffinityPropagation Affinity Propagation [is] most appropriate for small to medium sized datasets. 抛出的错误：
Traceback (most recent call last):
File "C:/Users/my.name/Documents/my files/Programs/clustering_test/SOexample.py", line 68, in <module>
aff.fit(hash_matrix)
File "C:\Python34\lib\site-packages\sklearn\cluster\affinity_propagation_.py", line 301, in fit
copy=self.copy, verbose=self.verbose, return_n_iter=True)
File "C:\Python34\lib\site-packages\sklearn\cluster\affinity_propagation_.py", line 105, in affinity_propagation
S += ((np.finfo(np.double).eps * S + np.finfo(np.double).tiny * 100) *
MemoryError
完整的工作代码示例：
import pandas as pd
import numpy as np
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn import cluster
data = ['10 news headlines', '3 current events in the news today',
'5 day break in new york', '7 breaking news', '7 news breaking news',
'7 news headlines', '7 news online', '7 news today', 'america current news',
'america new york time', 'america news latest', 'america news online',
'america news paper', 'america news today', 'america recent news',
'american news channel', 'american news channels', 'any news today',
'article about new york', 'article about new york city',
'article in newspaper today', 'article news today', 'article today news',
'articles about new york', 'articles on new york', 'articles usa',
'best news channel', 'best news homepage', 'best newspaper websites',
'big news stories', 'big news stories of 2013', 'break in new york',
'break news today', 'break to new york', 'breaking cnn news',
'breaking entertainment news', 'breaking global news', 'breaking headlines',
'breaking international news', 'breaking international news today',
'breaking latest news', 'breaking nation news', 'breaking new cnn',
'breaking new for today', 'breaking new of today', 'breaking new today',
'breaking news', 'breaking news america today',
'breaking news and top stories', 'breaking news around the world',
'breaking news around the world today', 'breaking news brooklyn',
'breaking news cnn', 'breaking news cnn alerts', 'breaking news cnn live',
'world important news today', 'world latest breaking news',
'world latest news', 'world latest news headlines', 'world latest news today',
'world latest news update', 'world latest news updates', 'world new headlines',
'world new now', 'world new today', 'world news', 'world news articles',
'world news articles today', 'world news breaking',
'world news breaking headlines', 'world news cnn today',
'world news current events', 'world news events', 'world news for this week',
'world news for today', 'world news headline', 'world news headlines',
'world news headlines daily nation', 'world news headlines today live',
'world news highlights', 'world news latest headlines', 'world news now',
'world news now cnn', 'world news recent', 'world news report',
'world news sites', 'world news sources', 'world news stories',
'world news today', 'world news today 2014', 'world news today cnn',
'world news today headlines', 'world news today live',
'world news today video', 'world news update', 'world news update today',
'world news updates', 'world news updates today', 'world news video',
'world news videos', 'world news website', 'world news websites',
'world newspaper', 'world newspaper articles', 'world newspaper online',
'world recent news', 'world times news', 'world today news', 'world top news',
'world top news today', 'world updated news', 'world wide latest news',
'world wide news today', 'worlds news', 'worlds news headlines',
'worlds news today', 'worldwide breaking news', 'worldwide news today',
'www.headline news today', 'www.headlines news', 'www.news headlines today',
'www.news today.in', 'www.today news paper.com', 'www.todays news headlines',
'www.todays news headlines.com', 'www.todays news.com',
'www.world latest news']
#data = pd.read_csv('myfile.csv')['SomeColumn'].drop_duplicates().reset_index(drop=True).to_frame()[:10000]
#data.columns = ['Keyword']
#data = data['Keyword'].tolist()
stemmer = PorterStemmer()
stemmed_data = [stemmer.stem_word(word) for word in data]
hasher = HashingVectorizer(stop_words='english', ngram_range=(1,2), analyzer='word')
hash_matrix = hasher.transform(stemmed_data)
aff = cluster.AffinityPropagation()
aff.fit(hash_matrix)
df = pd.DataFrame({'Keyword': data, 'Cluster': aff.labels_.tolist()})
grouped = df.groupby('Cluster').apply(lambda frame: frame['Keyword']).reset_index(1, drop=True).to_frame('Keyword')
答案 0 :(得分：5)
亲和传播需要二次记忆来存储全距离矩阵。
因此，如果您有10000个样本和双精度，则需要大约800,000,000个字节。如果在某个时刻需要复制此矩阵，则需要1.6 GB RAM（不包括输入数据和任何开销）。
如果你想要“数十万”，至少是另一个因素100，即80到160 GB RAM 。