python数据挖掘之K-Means 二分K-means K-means++ 以及DBSCAN算法的实战应用(超详细必看）

布满荆棘的人生 2023-09-30 16:04 14阅读 0赞

> **需要完整代码和数据集请点赞关注收藏后评论区留言**

**二分K-means以及K-means++是K-means的两种变体，可以解决K-means算法质心敏感，过拟合等等问题**

**运行环境是anaconda+pytorch**

项目结构如下

![10f41ab0dc294cc1b8325ec8c0564932.png][]

scutVec.npy中存放数据集

**main函数中你可以用不同的方法去跑不同的数据集来观察他们之间的区别**

**其他几个python文件的代码请点赞关注收藏请评论区留言**

main函数代码如下

import numpy as np
    from sklearn.preprocessing import StandardScaler, MinMaxScaler
    import matplotlib.pyplot as plt
    import sklearn.datasets as datasets
    import DBSCAN
    import KMeansRelevant
    
    
    def show_cluster_result(cluster, normal_data, cluster_method):
        # 第一幅图展示原始图
        plt.figure(figsize=(24, 10), dpi=80)
        plt.subplot(1, 2, 1)
        plt.scatter(normal_data[:, 0], normal_data[:, 1], color='black')
        plt.title('raw graph')
    
        # 第二幅图聚类结果
        plt.subplot(1, 2, 2)
        plt.scatter(normal_data[:, 0], normal_data[:, 1], c=cluster, marker='o')
        plt.title(cluster_method)
        plt.legend(labels=['x', 'y'])
        plt.show()
    
    def show_K_dis(data, K):
        distance = DBSCAN.k_nearest_neighbour_distance(dataSet, K)
        x = [i for i in range(distance.shape[0])]
        plt.scatter(x, distance)
        plt.title("Points Sorted by Distance to %ith Nearest Neighbor" % K)
        plt.legend()
        plt.show()
    
    
    scutJPG = np.load("scutVec.npy")  # 窝工校徽
    n_samples = 2500
    noisy_circles, _ = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05,random_state=8)
    noisy_moons, _ = datasets.make_moons(n_samples=n_samples, noise=0.05,random_state=8)
    blobs, _ = datasets.make_blobs(n_samples=n_samples, random_state=8)
    
    # 1,选择不同的数据集
    dataSet = scutJPG  # ['noisy_circles', 'noisy_moons', 'blobs', 'scutJPG']
    
    # 2, 对数据进行处理
    scaler = StandardScaler()
    normal_data = scaler.fit_transform(dataSet)
    
    # 3, 选择并运行聚类算法
    cluster_method = "kMeans"  # ['kMeans', 'biKMeans', 'kMeans++', 'DBSCAN']
    
    if cluster_method == "DBSCAN":
        # DBSCAN的参数设置与运行, 请根据K-dis图像来获得大致的eps值
        min_pts = 8
        # 查看k-dist
        show_K_dis(normal_data, min_pts)
        eps = 0.08  # 0.09
        cluster = DBSCAN.dbscan(normal_data, eps, min_pts)
    elif cluster_method == "biKMeans":  # 运行二分K-means算法，请指定k值
        cluster = KMeansRelevant.biKMeans(normal_data, 10)
    elif cluster_method == "kMeans++":  # 运行K-means++算法，请指定k值
        cluster = KMeansRelevant.kMeansPP(normal_data, 10)
    else: # 默认其他跑KMeans
        cluster = KMeansRelevant.origin_kMeans(normal_data, 10)
    
    # 4, 输出聚类结果
    num = cluster.max(0)
    print("cluster num is:", num + 1)
    show_cluster_result(cluster, normal_data, cluster_method)

运行结果如下

![6fe32af1dc804f2c9d689fb7ddcd8239.png][]

结果可视化如下

![95ee3d0ca96247ac930205d38f5cb418.png][]

![b0c53dcf45f64f428e0802326ddc3a0e.png][]

![40a9d1bbffce43a7a52c3f2f7db3d1a0.png][]

![9e72ccd37cba4c6d9243c95aec2c721f.png][]

**需要完整代码和数据集请点赞关注收藏后评论区留言**

[10f41ab0dc294cc1b8325ec8c0564932.png]: https://img-blog.csdnimg.cn/10f41ab0dc294cc1b8325ec8c0564932.png
[6fe32af1dc804f2c9d689fb7ddcd8239.png]: https://img-blog.csdnimg.cn/6fe32af1dc804f2c9d689fb7ddcd8239.png
[95ee3d0ca96247ac930205d38f5cb418.png]: https://img-blog.csdnimg.cn/95ee3d0ca96247ac930205d38f5cb418.png
[b0c53dcf45f64f428e0802326ddc3a0e.png]: https://img-blog.csdnimg.cn/b0c53dcf45f64f428e0802326ddc3a0e.png
[40a9d1bbffce43a7a52c3f2f7db3d1a0.png]: https://img-blog.csdnimg.cn/40a9d1bbffce43a7a52c3f2f7db3d1a0.png
[9e72ccd37cba4c6d9243c95aec2c721f.png]: https://img-blog.csdnimg.cn/9e72ccd37cba4c6d9243c95aec2c721f.png