聚类，分类问题？

Question

根据科罗拉多州罗斯国家森林区域树木类型的观测数据covtype.csv，实现树木类型识别任务

import pandas as pd
import numpy as np
import time
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

# 初始化质心
def initialize_centroids(data, k):
    indices = np.random.choice(len(data), k, replace=False)
    return data[indices]

# 分配数据点到最近的质心
def get_clusters(data, centroids):
    cluster_labels = np.zeros(len(data))
    for i, point in enumerate(data):
        distances = np.linalg.norm(point - centroids, axis=1)
        cluster_labels[i] = np.argmin(distances)
    return cluster_labels

# 更新质心
def update_centroids(data, cluster_labels, k):
    new_centroids = np.zeros((k, data.shape[1]))
    for i in range(k):
        cluster_points = data[cluster_labels == i]
        new_centroids[i] = np.mean(cluster_points, axis=0)
    return new_centroids

# K-Means聚类主函数
def k_means(data, k, T, epsilon):
    start = time.time()  # 开始时间，计时
    centroids = initialize_centroids(data, k)
    t = 0
    while t <= T:
        cluster_labels = get_clusters(data, centroids)

        new_centroids = update_centroids(data, cluster_labels, k)

        if np.linalg.norm(new_centroids - centroids) < epsilon:
            break
        centroids = new_centroids
        print("第", t, "次迭代")
        t += 1
    print("用时：{0}".format(time.time() - start))
    return cluster_labels, centroids

# 聚类结果的可视化
def visualize_clusters(data_scaled, cluster_labels):
    unique_clusters = np.unique(cluster_labels)
    colors = plt.cm.jet(np.linspace(0, 1, len(unique_clusters)))
    for i, cluster in enumerate(unique_clusters):
        cluster_data = data_scaled[cluster_labels == cluster]
        plt.scatter(cluster_data[:, 0], cluster_data[:, 1], c=colors[i], label=f'Cluster {i+1}')
    plt.title('K-Means Clustering')
    plt.xlabel('Feature 1')
    plt.ylabel('Feature 2')
    plt.legend()
    plt.grid(True)
    plt.show()

# 主程序
if __name__ == "__main__":
    # 读取数据集
    data_path = r'D:/360zip/机器学习实验课材料/covtype.csv' # 替换为您的实际文件路径
    data = pd.read_csv(data_path)

    # 数据预处理：缩放特征
    scaler = MinMaxScaler()
    data_scaled = scaler.fit_transform(data)

    # 执行K-Means聚类
    k = 7  # 聚类的数量
    T = 10  # 最大迭代次数
    epsilon = 1e-5  # 收敛阈值
    cluster_labels, centroids = k_means(data_scaled, k, T, epsilon)

    # 可视化聚类结果
    visualize_clusters(data_scaled, cluster_labels)

聚类之后，发现可视化的图不是自己想要的，我想要的是每个颜色都有自己的区域，但是运行结果不如意

到底要聚类还是分类啊

Answer 1

如果已经有各类树木的名称维度，用分类