Word embeddings are dense vectors used to represent word meanings. Both Stanford’s GloVe and Google’s word2vec are open source packages and provide efficient implementations to train these vectors. Many pretrained vectors can be found online. I download the pretrained word vectors from the GloVe web. The vectors look like below, in each line a word is represented as a multidimentional vector of float values:

```
pretrained = open("glove.6B/glove.6B.50d.txt", "r").readlines()
for line in pretrained[:3]:
print line
```

```
the 0.418 0.24968 ... -0.18411 -0.11514 -0.78581
, 0.013441 0.23682 ... -0.56657 0.044691 0.30392
. 0.15164 0.30177 ... -0.35652 0.016413 0.10216
```

I’m going to perform a clustering analysis on these vectors. First I prepare the vocabulary table to easily look up words and the embedding matrix for clustering:

```
import numpy
id2word = []
id2vec = []
for line in pretrained[:10000]:
data = line.split(" ")
id2word.append(data[0])
id2vec.append(numpy.array([float(d) for d in data[1:]]))
```

The scikit-learn package in Python provides a rich library of clustering algorithms. K-means is one of the most commonly used algorithms. You can customize the number of clusters to form with `n_clusters`

, here i choose 100.

```
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=100).fit(numpy.array(id2vec))
```

So the vocabularies in each cluster are:

```
word_cluster = []
for c in range(100):
word_cluster.append([])
for i, c in enumerate(kmeans.labels_):
word_cluster[c].append(id2word[i])
for words in word_cluster[:5]:
print words
```

```
['williams', 'mike', 'tom', 'bob', 'steve', ...]
['i', 'we', 'you', 'what', 'him', ...]
['visit', 'summer', 'events', 'trip', 'festival', ...]
['$', 'million', 'total', '100', 'estimated', ...]
['support', 'process', 'organization', 'effort', 'launched', ...]
```

It’s fun to discover semantically similar words using the method and adjusting the parameters. But it’s better if you first obtain a set of vectors which can perfectly represent word meanings!