@author: Yannis Mentekidis """ """ Read data """ import data_read as dtrd import numpy as np data, features, projects_true = dtrd.All(sparse=False) """ Processing """ import mypreprocessing as prp data = prp.TrimmingPresence(data, low_thresh=1, hig_thresh=70) #data = prp.LDAProjection(data, features=features, LDA_topics=12,verbose=True) #data = prp.RowWiseNorm(data) data = prp.tfidf(data) data = np.array(data.todense()) """ Clustering """ from sklearn.cluster import AgglomerativeClustering from sklearn import metrics dist_metric = "cosine" silh = [] h**o = [] comp = [] vmea = [] choices = range(2, 11) for c in choices:
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 20:43:07 2016
@author: Yannis Mentekidis, Themis Papavasileiou, Panos Siatos
"""
""" Load Data """
import data_read as dtrd
data, features, projects_true = dtrd.All(sparse=False)
""" Process Data """
import mypreprocessing as prp
import numpy as np
data = prp.TrimmingPresence(data, low_thresh=1, hig_thresh=70)
data_p = prp.tfidf(data)
data_p = np.array(data_p.todense())
""" Clustering with Hierarchical Algorithm """
from sklearn.cluster import AgglomerativeClustering
c=7
dist_metric='cosine'
clu = AgglomerativeClustering(n_clusters = c, affinity=dist_metric, linkage="average")
clu.fit(data_p)
for cluster in range(c):
print "-=-=-=-=-= Cluster %d -=-=-=-=-=" %(cluster)
indices = [i for i, x in enumerate(list(clu.labels_)) if x == cluster]
print indices
