使用DBSCAN算法，
我已经对一系列经纬度对进行了聚类，并使用matplotlib绘制了图形。在绘图时，它包括“噪点”坐标，也就是那些没有分配给270个创建的聚类之一的点。我想从图中删除噪点，并只绘制满足指定要求的聚类，但我不确定如何实现。我应该如何排除噪点（即那些未分配给聚类的点）？
以下是我用于聚类和绘图的代码：
我的目标是仅呈现聚类，黑色点表示噪声，未被DBSCAN输入定义的聚类所添加，而彩色点则表示聚类。
(DBSCAN(eps=epsilon, min_samples=10, algorithm='ball_tree', metric='haversine')
我已经对一系列经纬度对进行了聚类，并使用matplotlib绘制了图形。在绘图时，它包括“噪点”坐标，也就是那些没有分配给270个创建的聚类之一的点。我想从图中删除噪点，并只绘制满足指定要求的聚类，但我不确定如何实现。我应该如何排除噪点（即那些未分配给聚类的点）？
以下是我用于聚类和绘图的代码：
df = pd.read_csv('xxx.csv')
# define the number of kilometers in one radiation
# which will be used to convert esp from km to radiation
kms_per_rad = 6371.0088
# define a function to calculate the geographic coordinate
# centroid of a cluster of geographic points
# it will be used later to calculate the centroids of DBSCAN cluster
# because Scikit-learn DBSCAN cluster class does not come with centroid attribute.
def get_centroid(cluster):
"""calculate the centroid of a cluster of geographic coordinate points
Args:
cluster coordinates, nx2 array-like (array, list of lists, etc)
n is the number of points(latitude, longitude)in the cluster.
Return:
geometry centroid of the cluster
"""
cluster_ary = np.asarray(cluster)
centroid = cluster_ary.mean(axis=0)
return centroid
# convert eps to radians for use by haversine
epsilon = 0.1/kms_per_rad #1.5=1.5km 1=1km 0.5=500m 0.25=250m 0.1=100m
# Extract intersection coordinates (latitude, longitude)
tweet_coords = df.as_matrix(columns=['latitude','longitude'])
start_time = time.time()
dbsc = (DBSCAN(eps=epsilon, min_samples=10, algorithm='ball_tree', metric='haversine')
.fit(np.radians(tweet_coords)))
tweet_cluster_labels = dbsc.labels_
# get the number of clusters
num_clusters = len(set(dbsc.labels_))
# print the outcome
message = 'Clustered {:,} points down to {:,} clusters, for {:.1f}% compression in {:,.2f} seconds'
print(message.format(len(df), num_clusters, 100*(1 - float(num_clusters) / len(df)), time.time()-start_time))
print('Silhouette coefficient: {:0.03f}'.format(metrics.silhouette_score(tweet_coords, tweet_cluster_labels)))
# Turn the clusters into a pandas series,where each element is a cluster of points
dbsc_clusters = pd.Series([tweet_coords[tweet_cluster_labels==n] for n in range(num_clusters)])
# get centroid of each cluster
cluster_centroids = dbsc_clusters.map(get_centroid)
# unzip the list of centroid points (lat, lon) tuples into separate lat and lon lists
cent_lats, cent_lons = zip(*cluster_centroids)
# from these lats/lons create a new df of one representative point for eac cluster
centroids_df = pd.DataFrame({'longitude':cent_lons, 'latitude':cent_lats})
#print centroids_df
# Plot the clusters and cluster centroids
fig, ax = plt.subplots(figsize=[20, 12])
tweet_scatter = ax.scatter(df['longitude'], df['latitude'], c=tweet_cluster_labels, cmap = cm.hot, edgecolor='None', alpha=0.25, s=50)
centroid_scatter = ax.scatter(centroids_df['longitude'], centroids_df['latitude'], marker='x', linewidths=2, c='k', s=50)
ax.set_title('Tweet Clusters & Cluser Centroids', fontsize = 30)
ax.set_xlabel('Longitude', fontsize=24)
ax.set_ylabel('Latitude', fontsize = 24)
ax.legend([tweet_scatter, centroid_scatter], ['Tweets', 'Tweets Cluster Centroids'], loc='upper right', fontsize = 20)
plt.show()
我的目标是仅呈现聚类，黑色点表示噪声，未被DBSCAN输入定义的聚类所添加，而彩色点则表示聚类。