python - How to cluster data with user_id - k-means algorithm
问题描述
I want to cluster data of users by user_id
, because I need to analyze each cluster after clustering.
my clustering algorithm is k-means/k=3. I'm using python.
my data:
V1,V2
100,10
150,20
200,10
120,15
300,10
400,10
300,10
400,10
I removed user_id
column from this data. as far as I know that I should remove user_id
for k-means clustering.
my python code:
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from copy import deepcopy
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (16, 9)
plt.style.use('ggplot')
# Importing the dataset
data = pd.read_csv('C:/Users/S.M_Emamian/Desktop/xclara.csv')
print("Input Data and Shape")
print(data.shape)
data.head()
# Getting the values and plotting it
f1 = data['V1'].values
f2 = data['V2'].values
X = np.array(list(zip(f1, f2)))
plt.scatter(f1, f2, c='black', s=7)
# Euclidean Distance Caculator
def dist(a, b, ax=1):
return np.linalg.norm(a - b, axis=ax)
# Number of clusters
k = 3
# X coordinates of random centroids
C_x = np.random.randint(0, np.max(X)-20, size=k)
# Y coordinates of random centroids
C_y = np.random.randint(0, np.max(X)-20, size=k)
C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
print("Initial Centroids")
print(C)
# Plotting along with the Centroids
plt.scatter(f1, f2, c='#050505', s=7)
plt.scatter(C_x, C_y, marker='*', s=200, c='g')
# To store the value of centroids when it updates
C_old = np.zeros(C.shape)
# Cluster Lables(0, 1, 2)
clusters = np.zeros(len(X))
# Error func. - Distance between new centroids and old centroids
error = dist(C, C_old, None)
# Loop will run till the error becomes zero
while error != 0:
# Assigning each value to its closest cluster
for i in range(len(X)):
distances = dist(X[i], C)
cluster = np.argmin(distances)
clusters[i] = cluster
# Storing the old centroid values
C_old = deepcopy(C)
# Finding the new centroids by taking the average value
for i in range(k):
points = [X[j] for j in range(len(X)) if clusters[j] == i]
C[i] = np.mean(points, axis=0)
error = dist(C, C_old, None)
colors = ['r', 'g', 'b', 'y', 'c', 'm']
fig, ax = plt.subplots()
for i in range(k):
points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])
ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])
ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')
'''
==========================================================
scikit-learn
==========================================================
'''
from sklearn.cluster import KMeans
# Number of clusters
kmeans = KMeans(n_clusters=3)
# Fitting the input data
kmeans = kmeans.fit(X)
# Getting the cluster labels
labels = kmeans.predict(X)
# Centroid values
centroids = kmeans.cluster_centers_
# Comparing with scikit-learn centroids
print("Centroid values")
print("Scratch")
print(C) # From Scratch
print("sklearn")
print(centroids) # From sci-kit learn
my code works fine and it visualizes my data as well.
but I need to keep user_id
.
for example, I would like to know user_id=5
is Which of the clusters?
解决方案
只需在集群后添加 user_id 即可。
实际上,您可能想要做的恰恰相反:只需将集群标签添加到仍然具有集群标签的原始数据中。
只要您不更改数据顺序,这是一个微不足道的堆叠操作。
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