Djvu - 1 year ago 73

Python Question

I implement the kmeans algorithm in python, the code as following. I test the code use some simple data. just as following, which store in a file called data.txt

2 5

3 7

-1 -2

-3 -3

5 4

4 -4

3 -7

3.5 -9

my problem is that during the iteration, some cluster seem become empty, that is the (number of cluster) < k, and after my analysis, this seem will occure, but after search the web, I found no body deal this in the kmeans algorithm.

So I do not know where is the fault? is that because my test data is so simple

`import sys`

import numpy as np

from math import sqrt

"""

useage: python mykmeans.py mydata.txt k

"""

GAP = 2

MIN_VAL = 1000000

def get_distance(point1, point2):

dis = sqrt(pow(point1[0] - point2[0], 2) + pow(point1[1] - point2[1], 2))

return dis

def cluster_dis(centroid, cluster):

dis = 0.0

for point in cluster:

dis += get_distance(centroid, point)

return dis

def update_centroids(centroids, cluster_id, cluster):

x, y = 0.0, 0.0

length = len(cluster)

if length == 0: # TODO： this is my question? do we need to examine this?

return

for item in cluster:

x += item[0]

y += item[1]

centroids[cluster_id] = (x / length, y / length)

def kmeans(data, k):

assert k <= len(data)

seed_ids = np.random.randint(0, len(data), k)

centroids = [data[idx] for idx in seed_ids]

clusters = [[] for _ in xrange(k)]

cluster_idx = [-1] * len(data)

pre_dis = 0

while True:

for point_id, point in enumerate(data):

min_distance, tmp_id = MIN_VAL, -1

for seed_id, seed in enumerate(centroids):

distance = get_distance(seed, point)

if distance < min_distance:

min_distance = distance

tmp_id = seed_id

if cluster_idx[point_id] != -1:

dex = clusters[cluster_idx[point_id]].index(point)

del clusters[cluster_idx[point_id]][dex]

clusters[tmp_id].append(point)

cluster_idx[point_id] = tmp_id

now_dis = 0.0

for cluster_id, cluster in enumerate(clusters):

now_dis += cluster_dis(centroids[cluster_id], cluster)

update_centroids(centroids, cluster_id, cluster)

delta_dis = now_dis - pre_dis

pre_dis = now_dis

if delta_dis < GAP:

break

print(centroids)

print(clusters)

return centroids, clusters

def get_data(file_name):

try:

fr = open(file_name)

lines = fr.read().splitlines()

except IOError, e:

pass

finally:

fr.close()

data = []

for line in lines:

tmp = line.split()

x, y = float(tmp[0]), float(tmp[1])

data.append([x, y])

return data

def main():

args = sys.argv[1:]

assert len(args) > 1

file_name, k = args[0], int(args[1])

data = get_data(file_name)

kmeans(data, k)

if __name__ == '__main__':

main()

Answer Source

It is possible that k-means induces an empty cluster. Here is one example shown in figures. I also copied the figures below in case the link may expire some day.

The first figure below shows the distribution of the 7 points. Initially 3, 5, and 6 are selected as the cluster centers.

The '+' below shows the cluster centers changes after 1st iteration, and the same color indicates the corresponding points are in the same clusters.

From the figure below, you can see after 2 iterations, the blue cluster becomes empty, and there are indeed 2 clusters instead of the initialization value 3.

So the empty cluster probably due to the initialization and 'incorrect' cluster number. You may try different `k`

in your code and run the program several times to observe the clustering result, making it more robust.