In this post you will find K means clustering example with **word2vec** in python code. **Word2Vec** is one of the popular methods in language modeling and feature learning techniques in natural language processing (NLP). This method is used to create **word embeddings** in machine learning whenever we need vector representation of data.

For example in data clustering algorithms instead of **bag of words (BOW)** model we can use **Word2Vec**. The advantage of using Word2Vec is that it can capture the distance between individual words.

The example in this post will demonstrate how to use results of **Word2Vec word embeddings in clustering algorithms**. For this, Word2Vec model will be feeded into several K means clustering algorithms from NLTK and Scikit-learn libraries.

Here we will do clustering at word level. Our clusters will be groups of words. In case we need to cluster at sentence or paragraph level, here is the link that showing how to move from word level to sentence/paragraph level:

Text Clustering with Word Embedding in Machine Learning

There is also doc2vec word embedding model that is based on word2vec. doc2vec is created for embedding sentence/paragraph/document. Here is the link how to use doc2vec word embedding in machine learning:

Text Clustering with doc2vec Word Embedding Machine Learning Model

## Getting Word2vec

Using **word2vec** from python library **gensim** is simple and well described in tutorials and on the web [3], [4], [5]. Here we just look at basic example. For the input we use the sequence of sentences hard-coded in the script.

from gensim.models import Word2Vec sentences = [['this', 'is', 'the', 'good', 'machine', 'learning', 'book'], ['this', 'is', 'another', 'book'], ['one', 'more', 'book'], ['this', 'is', 'the', 'new', 'post'], ['this', 'is', 'about', 'machine', 'learning', 'post'], ['and', 'this', 'is', 'the', 'last', 'post'] model = Word2Vec(sentences, min_count=1)

Now we have model with words embedded. We can query model for similar words like below or ask to represent word as vector:

print (model.similarity('this', 'is')) print (model.similarity('post', 'book')) #output -0.0198180344218 #output -0.079446731287 print (model.most_similar(positive=['machine'], negative=[], topn=2)) #output: [('new', 0.24608060717582703), ('is', 0.06899910420179367)] print (model['the']) #output [-0.00217354 -0.00237131 0.00296396 ..., 0.00138597 0.00291924 0.00409528]

To get **vocabulary** or the number of words in vocabulary:

print (list(model.vocab)) print (len(list(model.vocab)))

This will produce: [‘good’, ‘this’, ‘post’, ‘another’, ‘learning’, ‘last’, ‘the’, ‘and’, ‘more’, ‘new’, ‘is’, ‘one’, ‘about’, ‘machine’, ‘book’]

Now we will feed word embeddings into **clustering algorithm** such as **k Means** which is one of the most popular unsupervised learning algorithms for finding interesting segments in the data. It can be used for separating customers into groups, combining documents into topics and for many other applications.

You will find below two k means clustering examples.

**K Means Clustering with NLTK Library**

Our first example is using k means algorithm from NLTK library.

To use word embeddings word2vec in machine learning clustering algorithms we initiate X as below:

X = model[model.vocab]

Now we can plug our X data into clustering algorithms.

from nltk.cluster import KMeansClusterer import nltk NUM_CLUSTERS=3 kclusterer = KMeansClusterer(NUM_CLUSTERS, distance=nltk.cluster.util.cosine_distance, repeats=25) assigned_clusters = kclusterer.cluster(X, assign_clusters=True) print (assigned_clusters) # output: [0, 2, 1, 2, 2, 1, 2, 2, 0, 1, 0, 1, 2, 1, 2]

In the python code above there are several options for the distance as below:

nltk.cluster.util.cosine_distance(u, v)

Returns 1 minus the cosine of the angle between vectors v and u. This is equal to 1 – (u.v / |u||v|).

nltk.cluster.util.euclidean_distance(u, v)

Returns the euclidean distance between vectors u and v. This is equivalent to the length of the vector (u – v).

Here we use cosine distance to cluster our data.

After we got cluster results we can associate each word with the cluster that it got assigned to:

words = list(model.vocab) for i, word in enumerate(words): print (word + ":" + str(assigned_clusters[i]))

Here is the output for the above:

good:0

this:2

post:1

another:2

learning:2

last:1

the:2

and:2

more:0

new:1

is:0

one:1

about:2

machine:1

book:2

## K Means Clustering with Scikit-learn Library

This example is based on k means from scikit-learn library.

from sklearn import cluster from sklearn import metrics kmeans = cluster.KMeans(n_clusters=NUM_CLUSTERS) kmeans.fit(X) labels = kmeans.labels_ centroids = kmeans.cluster_centers_ print ("Cluster id labels for inputted data") print (labels) print ("Centroids data") print (centroids) print ("Score (Opposite of the value of X on the K-means objective which is Sum of distances of samples to their closest cluster center):") print (kmeans.score(X)) silhouette_score = metrics.silhouette_score(X, labels, metric='euclidean') print ("Silhouette_score: ") print (silhouette_score)

In this example we also got some useful metrics to estimate clustering performance.

Output:

Cluster id labels for inputted data [0 1 1 ..., 1 2 2] Centroids data [[ -3.82586889e-04 1.39791325e-03 -2.13839358e-03 ..., -8.68172920e-04 -1.23599875e-03 1.80053393e-03] [ -3.11774168e-04 -1.63297475e-03 1.76715955e-03 ..., -1.43826099e-03 1.22940990e-03 1.06353679e-03] [ 1.91571176e-04 6.40696089e-04 1.38173658e-03 ..., -3.26442620e-03 -1.08828480e-03 -9.43636987e-05]] Score (Opposite of the value of X on the K-means objective which is Sum of distances of samples to their closest cluster center): -0.00894730946094 Silhouette_score: 0.0427737

Here is the full python code of the script.

# -*- coding: utf-8 -*- from gensim.models import Word2Vec from nltk.cluster import KMeansClusterer import nltk from sklearn import cluster from sklearn import metrics # training data sentences = [['this', 'is', 'the', 'good', 'machine', 'learning', 'book'], ['this', 'is', 'another', 'book'], ['one', 'more', 'book'], ['this', 'is', 'the', 'new', 'post'], ['this', 'is', 'about', 'machine', 'learning', 'post'], ['and', 'this', 'is', 'the', 'last', 'post']] # training model model = Word2Vec(sentences, min_count=1) # get vector data X = model[model.vocab] print (X) print (model.similarity('this', 'is')) print (model.similarity('post', 'book')) print (model.most_similar(positive=['machine'], negative=[], topn=2)) print (model['the']) print (list(model.vocab)) print (len(list(model.vocab))) NUM_CLUSTERS=3 kclusterer = KMeansClusterer(NUM_CLUSTERS, distance=nltk.cluster.util.cosine_distance, repeats=25) assigned_clusters = kclusterer.cluster(X, assign_clusters=True) print (assigned_clusters) words = list(model.vocab) for i, word in enumerate(words): print (word + ":" + str(assigned_clusters[i])) kmeans = cluster.KMeans(n_clusters=NUM_CLUSTERS) kmeans.fit(X) labels = kmeans.labels_ centroids = kmeans.cluster_centers_ print ("Cluster id labels for inputted data") print (labels) print ("Centroids data") print (centroids) print ("Score (Opposite of the value of X on the K-means objective which is Sum of distances of samples to their closest cluster center):") print (kmeans.score(X)) silhouette_score = metrics.silhouette_score(X, labels, metric='euclidean') print ("Silhouette_score: ") print (silhouette_score)

**References**

1. Word embedding

2. Comparative study of word embedding methods in topic segmentation

3. models.word2vec – Deep learning with word2vec

4. Word2vec Tutorial

5. How to Develop Word Embeddings in Python with Gensim

6. nltk.cluster package