Cluster data using k-means
'''
# whiten the observations
data_w = vq.whiten(data)
# create the classifier object
kmeans, labels = vq.kmeans2(data_w, k=4, iter=30)
# fit the data
return kmeans, labels
# the file name of the dataset
r_filename = '../../Data/Chapter04/bank_contacts.csv'
# read the data
csv_read = pd.read_csv(r_filename)
# select variables
selected = csv_read[[
'n_duration', 'n_nr_employed', 'prev_ctc_outcome_success', 'n_euribor3m',
'n_cons_conf_idx', 'n_age', 'month_oct', 'n_cons_price_idx',
'edu_university_degree', 'n_pdays', 'dow_mon', 'job_student',
'job_technician', 'job_housemaid', 'edu_basic_6y'
]]
# cluster the data
centroids, labels = findClusters_kmeans(selected.as_matrix())
hlp.printClustersSummary(selected, labels, centroids)
meanShift = cl.MeanShift(
bandwidth=bandwidth,
bin_seeding=True
)
# fit the data
return meanShift.fit(data)
# the file name of the dataset
r_filename = '../../Data/Chapter04/bank_contacts.csv'
# read the data
csv_read = pd.read_csv(r_filename)
# select variables
selected = csv_read[['n_duration','n_nr_employed',
'prev_ctc_outcome_success','n_euribor3m',
'n_cons_conf_idx','n_age','month_oct',
'n_cons_price_idx','edu_university_degree','n_pdays',
'dow_mon','job_student','job_technician',
'job_housemaid','edu_basic_6y']]
# cluster the data
cluster = findClusters_meanShift(selected.as_matrix())
# assess the clusters effectiveness
labels = cluster.labels_
centroids = cluster.cluster_centers_
hlp.printClustersSummary(selected, labels, centroids)