The quality of text-clustering depends mainly on two factors:
Some notion of similarity between the documents you want to cluster. For example, it s easy to distinguish between newsarticles about sports and politics in vector space via tfidf-cosine-distance. It s a lot harder to cluster product-reviews in "good" or "bad" based on this measure.
The clustering method itself. You know how many cluster there ll be? Ok, use kmeans. You don t care about accuracy but want to show a nice tree-structure for navigation of search-results? Use some kind of hierarchical clustering.
There is no text-clustering solution, that would work well under any circumstances. And therefore it s probably not enough to take some clustering software out of the box and throw your data at it.
Having said that, here s some experimental code i used some time ago to play around with text-clustering. The documents are represented as normalized tfidf-vectors and the similarity is measured as cosine distance. The clustering method itself is majorclust.
import sys
from math import log, sqrt
from itertools import combinations
def cosine_distance(a, b):
cos = 0.0
a_tfidf = a["tfidf"]
for token, tfidf in b["tfidf"].iteritems():
if token in a_tfidf:
cos += tfidf * a_tfidf[token]
return cos
def normalize(features):
norm = 1.0 / sqrt(sum(i**2 for i in features.itervalues()))
for k, v in features.iteritems():
features[k] = v * norm
return features
def add_tfidf_to(documents):
tokens = {}
for id, doc in enumerate(documents):
tf = {}
doc["tfidf"] = {}
doc_tokens = doc.get("tokens", [])
for token in doc_tokens:
tf[token] = tf.get(token, 0) + 1
num_tokens = len(doc_tokens)
if num_tokens > 0:
for token, freq in tf.iteritems():
tokens.setdefault(token, []).append((id, float(freq) / num_tokens))
doc_count = float(len(documents))
for token, docs in tokens.iteritems():
idf = log(doc_count / len(docs))
for id, tf in docs:
tfidf = tf * idf
if tfidf > 0:
documents[id]["tfidf"][token] = tfidf
for doc in documents:
doc["tfidf"] = normalize(doc["tfidf"])
def choose_cluster(node, cluster_lookup, edges):
new = cluster_lookup[node]
if node in edges:
seen, num_seen = {}, {}
for target, weight in edges.get(node, []):
seen[cluster_lookup[target]] = seen.get(
cluster_lookup[target], 0.0) + weight
for k, v in seen.iteritems():
num_seen.setdefault(v, []).append(k)
new = num_seen[max(num_seen)][0]
return new
def majorclust(graph):
cluster_lookup = dict((node, i) for i, node in enumerate(graph.nodes))
count = 0
movements = set()
finished = False
while not finished:
finished = True
for node in graph.nodes:
new = choose_cluster(node, cluster_lookup, graph.edges)
move = (node, cluster_lookup[node], new)
if new != cluster_lookup[node] and move not in movements:
movements.add(move)
cluster_lookup[node] = new
finished = False
clusters = {}
for k, v in cluster_lookup.iteritems():
clusters.setdefault(v, []).append(k)
return clusters.values()
def get_distance_graph(documents):
class Graph(object):
def __init__(self):
self.edges = {}
def add_edge(self, n1, n2, w):
self.edges.setdefault(n1, []).append((n2, w))
self.edges.setdefault(n2, []).append((n1, w))
graph = Graph()
doc_ids = range(len(documents))
graph.nodes = set(doc_ids)
for a, b in combinations(doc_ids, 2):
graph.add_edge(a, b, cosine_distance(documents[a], documents[b]))
return graph
def get_documents():
texts = [
"foo blub baz",
"foo bar baz",
"asdf bsdf csdf",
"foo bab blub",
"csdf hddf kjtz",
"123 456 890",
"321 890 456 foo",
"123 890 uiop",
]
return [{"text": text, "tokens": text.split()}
for i, text in enumerate(texts)]
def main(args):
documents = get_documents()
add_tfidf_to(documents)
dist_graph = get_distance_graph(documents)
for cluster in majorclust(dist_graph):
print "========="
for doc_id in cluster:
print documents[doc_id]["text"]
if __name__ == __main__ :
main(sys.argv)
For real applications, you would use a decent tokenizer, use integers instead of token-strings and don t calc a O(n^2) distance-matrix...
