clarans算法(基于选择的算法)python代码
时间: 2023-11-21 20:02:51 浏览: 87
Clarans算法是一种基于选择的聚类算法,它主要用于解决数据挖掘领域中的聚类问题。该算法的Python代码实现如下:
```python
# 导入相关库
import numpy as np
class Clarans:
def __init__(self, data, num_clusters, num_local, max_neighbours):
self.data = data
self.num_clusters = num_clusters
self.num_local = num_local
self.max_neighbours = max_neighbours
self.clusters = []
self.best_cost = float('inf')
def fit(self):
# 初始化聚类中心
initial_centers = np.random.choice(self.data, size=self.num_clusters, replace=False)
for _ in range(self.num_local):
current_centers = np.copy(initial_centers)
while True:
# 随机选择一个簇和一个邻居
idx = np.random.choice(range(self.num_clusters))
neighbour = np.random.randint(len(self.data))
if neighbour not in current_centers:
temp_centers = current_centers.copy()
temp_centers[idx] = self.data[neighbour]
cost = self.calculate_cost(temp_centers)
if cost < self.best_cost:
current_centers = temp_centers
self.best_cost = cost
self.clusters.append(current_centers)
def calculate_cost(self, centers):
cost = 0
for point in self.data:
distances = [np.linalg.norm(point - center) for center in centers]
cost += min(distances)
return cost
# 使用示例
data = np.array([[1, 2], [1, 3], [2, 2], [8, 7], [8, 8], [9, 8]])
clarans = Clarans(data, num_clusters=2, num_local=10, max_neighbours=3)
clarans.fit()
print(clarans.clusters)
```
以上就是Clarans算法的Python实现代码。通过不断随机选择簇和邻居,并计算总成本,最终得到最优的聚类中心点。在示例中,我们使用了一个简单的二维数据集进行演示。在实际应用中,我们可以根据具体需求调整参数以获得更好的聚类效果。
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