1. Read csv data to do dbscan analysis
Read the corresponding columns in the csv file, and then transformed, processed for the format required by this algorithm, and then dbscan operation, the current public code is also more, this paper according to the public code modification, the
The specific code is as follows:
from sklearn import datasets
import numpy as np
import random
import as plt
import time
import copy
import pandas as pd
# from import load_iris
def find_neighbor(j, x, eps):
N = list()
for i in range([0]):
temp = (((x[j] - x[i]))) # Calculate the Euclidean distance
if temp <= eps:
(i)
return set(N)
def DBSCAN(X, eps, min_Pts):
k = -1
neighbor_list = [] # Used to keep the neighborhood of each data
omega_list = [] # Core set of objects
gama = set([x for x in range(len(X))]) # Initially mark all points as unvisited
cluster = [-1 for _ in range(len(X))] # Clustering
for i in range(len(X)):
neighbor_list.append(find_neighbor(i, X, eps))
if len(neighbor_list[-1]) >= min_Pts:
omega_list.append(i) # Add samples to the core object set
omega_list = set(omega_list) # Converted to collections for ease of manipulation
while len(omega_list) > 0:
gama_old = (gama)
j = (list(omega_list)) # A randomly selected core object
k = k + 1
Q = list()
(j)
(j)
while len(Q) > 0:
q = Q[0]
(q)
if len(neighbor_list[q]) >= min_Pts:
delta = neighbor_list[q] & gama
deltalist = list(delta)
for i in range(len(delta)):
(deltalist[i])
gama = gama - delta
Ck = gama_old - gama
Cklist = list(Ck)
for i in range(len(Ck)):
cluster[Cklist[i]] = k
omega_list = omega_list - Ck
return cluster
# X = load_iris().data
data = pd.read_csv("")
x,y=data['Time (sec)'],data['Height (m HAE)']
print(type(x))
n=len(x)
x=(x)
x=(n,1)
y=(y)
y=(n,1)
X = ((x, y))
cluster_std=[[.1]], random_state=9)
eps = 0.08
min_Pts = 5
begin = ()
C = DBSCAN(X, eps, min_Pts)
end = ()
()
(X[:, 0], X[:, 1], c=C)
()2. Output result display
Modify the parameter display:
eps = 0.8 min_Pts = 5
3. Computational efficiency
When using a small amount of data to calculate the efficiency problem is not obvious, with the increase in the amount of data, the problem of computational efficiency becomes particularly obvious, it is difficult to meet the needs of a large number of data calculations, the later will try to find ways to optimize the calculation method or collection of C + + + code to optimize it.
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