如何为 sklearn 聚类算法准备 pandas 字符串数据 table？

Question

群组	角色	用户	出现次数
格斯	DEFAULT_M	馅饼	47251
RSS	DEFAULT_R	馅饼	27057
RRD	DEFAULT_M	达纳特	21251
注意事项	DEFAULT_R	博尼	17933
GTS	DEFAULT_Q	博尼	16067

我有大约 5000 行像上面这样的数据，我正在尝试制作一个聚类算法来了解哪些用户属于某个组。它将创建一个包含用户的组集群。当我尝试使用 sklearn 库来制作聚类算法时，不幸的是它告诉我数据需要是 int 或 float。它找不到这些词之间的距离。有没有办法我仍然可以在这些字符串数据帧上使用 sklearn k-means 算法来聚类用户组？另一种方法是将组和用户转换为数字，这将花费很长时间，我需要保留组和用户的字典。如果我这样做，是否有更简单的方法将组和用户转换为数字以便聚类算法可以解释？预先感谢您的帮助

Answer 1

据我所知，每个算法都适用于数字，或者将文本转换为数字，然后完成它的工作。也许你可以试试这个。

import numpy as np
from sklearn.cluster import AffinityPropagation
import distance
    
words = 'XYZ,LDPELDKSL,DFKLKSLFD,ABC,DLFKFKDLD,XYZ,LDPELDKSL,DFKLKSLFD,ABC,DLFKFKDLD,XYZ,LDPELDKSL,XYZ,LDPELDKSL,DFKLKSLFD,ABC,DLFKFKDLD,XYZ,LDPELDKSL,DFKLKSLFD,ABC,DLFKFKDLD,XYZ,LDPELDKSL'.split(',') #Replace this line
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 = AffinityPropagation(affinity="precomputed", damping=0.5)
affprop.fit(lev_similarity)
for cluster_id in np.unique(affprop.labels_):
    exemplar = words[affprop.cluster_centers_indices_[cluster_id]]
    cluster = np.unique(words[np.nonzero(affprop.labels_==cluster_id)])
    cluster_str = ", ".join(cluster)
    print(" - *%s:* %s" % (exemplar, cluster_str))

结果：

- *LDPELDKSL:* LDPELDKSL
 - *DFKLKSLFD:* DFKLKSLFD
 - *XYZ:* ABC, XYZ
 - *DLFKFKDLD:* DLFKFKDLD

或者...

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score

documents = ["This little kitty came to play when I was eating at a restaurant.",
             "Merley has the best squooshy kitten belly.",
             "Google Translate app is incredible.",
             "If you open 100 tab in google you get a smiley face.",
             "Best cat photo I've ever taken.",
             "Climbing ninja cat.",
             "Impressed with google map feedback.",
             "Key promoter extension for Google Chrome."]

vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)

true_k = 8
model = KMeans(n_clusters=true_k, init='k-means++', max_iter=1000, n_init=1)
model.fit(X)

print("Top terms per cluster:")
order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
for i in range(true_k):
    print("Cluster %d:" % i),
    for ind in order_centroids[i, :10]:
        print(' %s' % terms[ind]),
    print

print("\n")
print("Prediction")

Y = vectorizer.transform(["chrome browser to open."])
prediction = model.predict(Y)
print(prediction)

Y = vectorizer.transform(["My cat is hungry."])
prediction = model.predict(Y)
print(prediction)

结果...每个集群的热门术语：

Cluster 0:
 kitten
 belly
 squooshy
 merley
 best
 eating
 google
 feedback
 face
 extension
Cluster 1:
 impressed
 map
 feedback
 google
 ve
 eating
 face
 extension
 climbing
 key
Cluster 2:
 climbing
 ninja
 cat
 eating
 impressed
 google
 feedback
 face
 extension
 ve
Cluster 3:
 eating
 kitty
 little
 came
 restaurant
 play
 ve
 feedback
 face
 extension
Cluster 4:
 100
 open
 tab
 smiley
 face
 google
 feedback
 extension
 eating
 climbing
Cluster 5:
 chrome
 extension
 promoter
 key
 google
 eating
 impressed
 feedback
 face
 ve
Cluster 6:
 translate
 app
 incredible
 google
 eating
 impressed
 feedback
 face
 extension
 ve
Cluster 7:
 ve
 taken
 photo
 best
 cat
 eating
 google
 feedback
 face
 extension

如何为 sklearn 聚类算法准备 pandas 字符串数据 table？

How to prepare pandas string data table for sklearn clustering algorithm?

python

cluster-analysis

k-means

scikit-learn

data-science