import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

def perform_kmeans_clustering(data, data_name, n_clusters, n_init, max_iter):
    x = data[data_name].to_numpy()
    kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
    y_kmeans = kmeans.fit_predict(x)

    fig = plt.figure()
    if len(data_name) == 2:
        ax = fig.add_subplot(projection='rectilinear')
        plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
        centers = kmeans.cluster_centers_
        plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
    else:
        ax = fig.add_subplot(projection='3d')
        ax.scatter(x[:, 0], x[:, 1], x[:, 2], c=y_kmeans, s=50, cmap="viridis")
        centers = kmeans.cluster_centers_
        ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
    return fig