Adaptive DBSCAN achievement
I am doing the DBSCAN clustering in python. I want to achieve an adaptive way to return the number of clusters by self calculating its eps and Minpts parameters. Below is my code.
import math
import copy
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
def loadDataSet(fileName, splitChar='\t'):
dataSet = []
with open(fileName) as fr:
for line in fr.readlines():
curline = line.strip().split(splitChar)
fltline = list(map(float, curline))
dataSet.append(fltline)
return dataSet
def dist(a,b):
return math.sqrt(math.pow(a[0]-b[0],2) + math.pow(a[1]-b[1],2))
def returnDk(matrix,k):
Dk = []
for i in range(len(matrix)):
Dk.append(matrix[i][k])
return Dk
def returnDkAverage(Dk):
sum = 0
for i in range(len(Dk)):
sum = sum + Dk[i]
return sum/len(Dk)
def CalculateDistMatrix(dataset):
DistMatrix = [[0 for j in range(len(dataset))] for i in range(len(dataset))]
for i in range(len(dataset)):
for j in range(len(dataset)):
DistMatrix[i][j] = dist(dataset[i], dataset[j])
return DistMatrix
def returnEpsCandidate(dataSet):
DistMatrix = CalculateDistMatrix(dataSet)
tmp_matrix = copy.deepcopy(DistMatrix)
for i in range(len(tmp_matrix)):
tmp_matrix[i].sort()
EpsCandidate = []
for k in range(1,len(dataSet)):
Dk = returnDk(tmp_matrix,k)
DkAverage = returnDkAverage(Dk)
EpsCandidate.append(DkAverage)
return EpsCandidate
def returnMinptsCandidate(DistMatrix,EpsCandidate):
MinptsCandidate = []
for k in range(len(EpsCandidate)):
tmp_eps = EpsCandidate[k]
tmp_count = 0
for i in range(len(DistMatrix)):
for j in range(len(DistMatrix[i])):
if DistMatrix[i][j] <= tmp_eps:
tmp_count = tmp_count + 1
MinptsCandidate.append(tmp_count/len(dataSet))
return MinptsCandidate
def returnClusterNumberList(dataset,EpsCandidate,MinptsCandidate):
np_dataset = np.array(dataset)
ClusterNumberList = []
for i in range(len(EpsCandidate)):
clustering = DBSCAN(eps= EpsCandidate[i],min_samples= MinptsCandidate[i]).fit(np_dataset)
num_clustering = max(clustering.labels_)
ClusterNumberList.append(num_clustering)
return ClusterNumberList
if __name__ == '__main__':
data = pd.read_csv('/Users/Desktop/Mic/recorder_test1/New folder/MFCCresultsforclustering/MFCCresultsforclustering.csv')
dataSet = data.iloc[:,0:13].values
EpsCandidate = returnEpsCandidate(dataSet)
DistMatrix = CalculateDistMatrix(dataSet)
MinptsCandidate = returnMinptsCandidate(DistMatrix,EpsCandidate)
ClusterNumberList = returnClusterNumberList(dataSet,EpsCandidate,MinptsCandidate)
print(EpsCandidate)
print(MinptsCandidate)
print('cluster number list is')
print(ClusterNumberList)
However, the output with the loading data set is all [-1]s. I am wondering where is the mistake. Am I right for this general direction? If not, how can I achieve the adaptive DBSCAN clustering?