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关于数据挖掘:Python用KShape对时间序列进行聚类和肘方法确定最优聚类数k可视化附代码数据

jiezi

全文链接：http://tecdat.cn/?p=27078

最近咱们被客户要求撰写对于 KShape 的钻研报告，包含一些图形和统计输入。

时序数据的聚类办法，该算法依照以下流程执行。

  1. 应用基于相互关测量的间隔标度（基于形态的间隔：SBD）
  2. 依据 1 计算工夫序列聚类的质心。（一种新的基于质心的聚类算法，可保留工夫序列的形态）
  3. 划分成每个簇的办法和个别的 kmeans 一样，然而在计算间隔尺度和重心的时候应用下面的 1 和 2。
import pandas as pd
    # 读取数据帧，将其转化为工夫序列数组，并将其存储在一个列表中    tata = []    for i, df in enmee(dfs):

        

        # 查看每个工夫序列数据的最大长度。for ts in tsda:

            if len(s) > ln_a:

                lenmx = len(ts)

        

        # 给出最初一个数据，以调整工夫序列数据的长度        for i, ts in enumerate(tsdata):

            dta[i] = ts + [ts[-1]] * n_dd

    





    # 转换为矢量    stack_list = []    for j in range(len(timeseries_dataset)):

       

        stack_list.append(data)

    

    # 转换为一维数组    trasfome_daa = np.stack(ack_ist, axis=0)

    return trafoed_data

数据集筹备

# 文件列表 flnes= soted(go.ob('mpldat/smeda*.csv'))
# 从文件中加载数据帧并将其存储在一个列表中。for ienme in fiemes:

    df = pd.read_csv(filnme, indx_cl=one,hadr=0)    flt.append(df)

聚类后果的可视化

# 为了计算穿插关系，须要对它们进行归一化解决。# TimeSeriesScalerMeanVariance 将是对数据进行规范化的类。sac_da = TimeeiesalerMVarne(mu=0.0, std=1.0).fit_trnform(tranfome_data)# KShape 类的实例化。ks = KShpe(_clusrs=2, n_nit=10, vrboe=True, rano_stte=sed)

yprd = ks.ft_reitsak_ata)# 聚类和可视化 plt.tight_layout()

plt.show()

点击题目查阅往期内容

R 语言 k -Shape 工夫序列聚类办法对股票价格工夫序列聚类

左右滑动查看更多

01

02

03

04

用肘法计算簇数

  • 什么是肘法...
  • 计算从每个点到簇核心的间隔的平方和，指定为簇内误差平方和 (SSE)。

  • 它是一种更改簇数，绘制每个 SSE 值，并将像“肘”一样蜿蜒的点设置为最佳簇数的办法。

    # 计算到 1~10 个群组 for i  in range(1,11):

    #进行聚类计算。

    ks.fit(sacdta)

    #KS.fit 给出 KS.inrta_    disorons.append(ks.netia_)

plt.plot(range(1,11), disorins, marker=’o’)


![图片](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/6a1ccbea12254f3d97fa81a99e98bfab~tplv-k3u1fbpfcp-zoom-1.image)

![图片](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/298a79a5376b426eac52746d0e36085a~tplv-k3u1fbpfcp-zoom-1.image)

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![图片](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/aa8d6ccca81a4219a91ca89c6a2bebbb~tplv-k3u1fbpfcp-zoom-1.image)

点击文末 **“浏览原文”**

获取全文残缺材料。本文选自《**Python 用 KShape 对工夫序列进行聚类和肘办法确定最优聚类数 k 可视化 **》。** 点击题目查阅往期内容 **

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