k-means算法与pca结合对多特征组聚类

数据：6个画像，每个画像中有700个标签。
解释：同一标签下不同画像对应的数据，我要对此数据做聚类，

结果展示：

后端代码显示

def kmeans_img(request):
	#获取前段数据
	UPLOAD_ROOT=os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'static/img')
	brand_name = request.POST.get('brand_name', '').strip()
	package_name_arr = request.POST.getlist('package_name_arr[]', [])
	label_lists = request.POST.getlist('label_lists[]', [])
	font_name =request.POST.get('font_name', '0').strip()
	K_value =request.POST.get('K_value', '2').strip()

	#局部放大后设置每个轴的最大最小值
	x_min =request.POST.get('x_min', None)
	x_max =request.POST.get('x_max', None)
	y_min =request.POST.get('y_min', None)
	y_max =request.POST.get('y_max', None)

	font_size_diy=int(request.POST.get('font_size_diy')) if request.POST.get('font_size_diy') else 7
	font_color_diy=request.POST.get('font_color_diy') if request.POST.get('font_color_diy') else '000000'
	font_color_diy='#'+font_color_diy
	point_size_diy=int(request.POST.get('point_size_diy')) if request.POST.get('point_size_diy') else 5


	# 挖除的包的id列表
	except_packages_list = request.POST.getlist('except_packages_list[]', [])
	# 挖除的包的name列表
	except_checked_packname_arr = request.POST.getlist('except_checked_packname_arr[]', [])

	# 1为重新聚类 0为挖除点
	check_loop_k =request.POST.get('check_loop_k', '0').strip()
	if check_loop_k is '1':
		package_name_arr = list(set(package_name_arr).difference(set(except_packages_list)))

	packages =CrowdPerspective.objects.filter(brand=brand_name,package_id__in=package_name_arr,label__in=label_lists).values("package_name","package_id","package_count").order_by("package_name").annotate(total_pages=Count("package_id")).all()
	print('*****')
	packages_df