37 lines
980 B
Python
37 lines
980 B
Python
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from scipy.io import arff
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import numpy as np
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from sklearn.cluster import KMeans
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from sklearn.datasets import make_blobs
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import matplotlib.pyplot as plt
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from sklearn import metrics
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.cluster import DBSCAN
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import hdbscan
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n_clusters = 2
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data = arff.loadarff('diamond9.arff')[0]
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data_final = []
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x_list = []
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y_list = []
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for (x, y, z) in data :
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x_list.append(x)
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y_list.append(y)
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data_final.append([x,y])
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clustering = AgglomerativeClustering(n_clusters = n_clusters, linkage='average').fit(data_final)
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colors = clustering.labels_
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plt.scatter(x_list, y_list, c=colors, s=5)
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plt.show()
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silh = metrics.silhouette_score(data_final, colors, metric='euclidean')
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dbsc = metrics.davies_bouldin_score(data_final, colors)
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caha = metrics.calinski_harabasz_score(data_final, colors)
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print("Coefficient de silhouette : ", silh)
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print("Indice de Davies Bouldin : ", dbsc)
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print("Indice de calinski harabasz : ", caha)
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