Python 使用levenahtein将大文件群集到3组
嗨,我有一个小文件和一个大文件, 这里的代码甚至不适用于大文件,只适用于小文件,因此如何读取大文件并对其执行操作?当我阅读并尝试在一个循环中进行集群时,它不起作用,因为每次迭代都是在线的。 下面是小文件的问题: 行的文件,我需要将它们分为3组。 我尝试过亲和传播,但它没有得到组大小参数,它给了我4个组,而第4个组只有一个词与另一个组非常接近:Python 使用levenahtein将大文件群集到3组,python,file,machine-learning,cluster-computing,k-means,Python,File,Machine Learning,Cluster Computing,K Means,嗨,我有一个小文件和一个大文件, 这里的代码甚至不适用于大文件,只适用于小文件,因此如何读取大文件并对其执行操作?当我阅读并尝试在一个循环中进行集群时,它不起作用,因为每次迭代都是在线的。 下面是小文件的问题: 行的文件,我需要将它们分为3组。 我尝试过亲和传播,但它没有得到组大小参数,它给了我4个组,而第4个组只有一个词与另一个组非常接近: 0 - *Bras5emax Estates, L.T.D. :* Bras5emax Estates, L.T.D. 1 - *BOZEMAN E
0
- *Bras5emax Estates, L.T.D.
:* Bras5emax Estates, L.T.D.
1
- *BOZEMAN Enterprises
:* BBAZEMAX ESTATES, LTD
, BOZEMAN Ent.
, BOZEMAN Enterprises
, BOZERMAN ENTERPRISES
, BRAZEMAX ESTATYS, LTD
, Bozeman Enterprises
2
- *PC Adelman
:* John Smith
, Michele LTD
, Nadelman, Jr
, PC Adelman
3
- *Gramkai, Inc.
:* Gramkai Books
, Gramkai, Inc.
, Gramkat Estates, Inc., Gramkat, Inc.
然后我尝试了K-MEANS,但结果是:
0
- *Gramkai Books
, Gramkai, Inc.
, Gramkat Estates, Inc., Gramkat, Inc.
:*
1
- *BBAZEMAX ESTATES, LTD
, BOZEMAN Enterprises
, BOZERMAN ENTERPRISES
, BRAZEMAX ESTATYS, LTD
, Bozeman Enterprises
, Bras5emax Estates, L.T.D.
:*
2
- *BOZEMAN Ent.
, John Smith
, Michele LTD
, Nadelman, Jr
, PC Adelman
:*
正如你所看到的,博兹曼·恩特。属于第2组,而不是第1组
我的问题是:有没有办法做得更好?K-MEANS中是否有集群中心
守则:
import numpy as np
import sklearn.cluster
import distance
f = open("names.txt", "r")
words = f.readlines()
words = np.asarray(words) #So that indexing with a list will work
lev_similarity = -1*np.array([[distance.levenshtein(w1,w2) for w1 in words] for w2 in words])
affprop = sklearn.cluster.KMeans(n_clusters=3)
affprop.fit(lev_similarity)
for cluster_id in np.unique(affprop.labels_):
print(cluster_id)
cluster = np.unique(words[np.nonzero(affprop.labels_==cluster_id)])
cluster_str = ", ".join(cluster)
print(" - *%s:*" % ( cluster_str))
可以通过几种方式改进给定文本名称(企业)的聚类
sorensen
或jaccard
,它们已经标准化了words = \
["Gramkai Books",
"Gramkai, Inc.",
"Gramkat Estates, Inc.",
"Gramkat, Inc.",
"BBAZEMAX ESTATES, LTD",
"BOZEMAN Enterprises",
"BOZERMAN ENTERPRISES",
"BRAZEMAX ESTATYS, LTD",
"Bozeman Enterprises",
"Bras5emax Estates, L.T.D.",
"BOZEMAN Ent.",
"John Smith",
"Michele LTD",
"Nadelman, Jr",
"PC Adelman"]
import re
import sklearn
from sklearn import cluster
words = [re.sub(r"(,|\.|ltd|l\.t\.d|inc|estates|enterprises|ent|estatys)","", w.lower()).strip() for w in words]
words = np.asarray(words) #So that indexing with a list will work
lev_similarity = -1*np.array([[distance.nlevenshtein(w1,w2,method = 1) for w1 in words] for w2 in words])
affprop = sklearn.cluster.KMeans(n_clusters=3)
affprop.fit(lev_similarity)
for cluster_id in np.unique(affprop.labels_):
print(cluster_id)
cluster = np.unique(words[np.nonzero(affprop.labels_==cluster_id)])
cluster_str = ", ".join(cluster)
print(" - *%s:*" % ( cluster_str))
结果:
0
- *john smith, michele, nadelman jr, pc adelman:*
1
- *bbazemax, bozeman, bozerman, bras5emax, brazemax:*
2
- *gramkai, gramkai books, gramkat:*
最后,您可能需要将更改的名称与原始名称连接起来。可以通过几种方式改进给定文本名称(企业)的聚类
sorensen
或jaccard
,它们已经标准化了words = \
["Gramkai Books",
"Gramkai, Inc.",
"Gramkat Estates, Inc.",
"Gramkat, Inc.",
"BBAZEMAX ESTATES, LTD",
"BOZEMAN Enterprises",
"BOZERMAN ENTERPRISES",
"BRAZEMAX ESTATYS, LTD",
"Bozeman Enterprises",
"Bras5emax Estates, L.T.D.",
"BOZEMAN Ent.",
"John Smith",
"Michele LTD",
"Nadelman, Jr",
"PC Adelman"]
import re
import sklearn
from sklearn import cluster
words = [re.sub(r"(,|\.|ltd|l\.t\.d|inc|estates|enterprises|ent|estatys)","", w.lower()).strip() for w in words]
words = np.asarray(words) #So that indexing with a list will work
lev_similarity = -1*np.array([[distance.nlevenshtein(w1,w2,method = 1) for w1 in words] for w2 in words])
affprop = sklearn.cluster.KMeans(n_clusters=3)
affprop.fit(lev_similarity)
for cluster_id in np.unique(affprop.labels_):
print(cluster_id)
cluster = np.unique(words[np.nonzero(affprop.labels_==cluster_id)])
cluster_str = ", ".join(cluster)
print(" - *%s:*" % ( cluster_str))
结果:
0
- *john smith, michele, nadelman jr, pc adelman:*
1
- *bbazemax, bozeman, bozerman, bras5emax, brazemax:*
2
- *gramkai, gramkai books, gramkat:*
最后,您可能需要将更改的名称与原始名称连接起来