cluster_label = 0

NOISE = -1

UNMARKED = 777777

stack = []

# initialize each point with unmarked

df['cluster'] = UNMARKED

# for each point in database

for index, point in df.iterrows():

if df.loc[index]['cluster'] == UNMARKED:

neighborhood = retrieve_neighbors(index, df, spatial_threshold, temporal_threshold)

if len(neighborhood) < min_neighbors:

df.set_value(index, 'cluster', NOISE)

else: # found a core point

cluster_label = cluster_label + 1

df.set_value(index, 'cluster', cluster_label)# assign a label to core point

for neig_index in neighborhood: # assign core's label to its neighbourhood

df.set_value(neig_index, 'cluster', cluster_label)

stack.append(neig_index) # append neighbourhood to stack

while len(stack) > 0: # find new neighbors from core point neighbourhood

current_point_index = stack.pop()

new_neighborhood = retrieve_neighbors(current_point_index, df, \

spatial_threshold, temporal_threshold)

if len(new_neighborhood) >= min_neighbors: # current_point is a new core

for neig_index in new_neighborhood:

neig_cluster = df.loc[neig_index]['cluster']

if (neig_cluster != NOISE) & (neig_cluster == UNMARKED):

df.set_value(neig_index, 'cluster', cluster_label)

stack.append(neig_index)

neigborhood = []

center_point = df.loc[index_center]

# filter by time

min_time = center_point['unix'] - temporal_threshold

max_time = center_point['unix'] + temporal_threshold

df = df[(df['unix'] >= min_time) & (df['unix'] <= max_time)]

# filter by distance

for index, point in df.iterrows():

if index != index_center:

distance = great_circle((center_point['latitude'], center_point['longitude']), \

(point['latitude'], point['longitude'])).meters

if distance <= spatial_threshold:

neigborhood.append(index)

return neigborhood