实现150条数据的 k-means算法聚类分析

1.从n个数据对象任意选择k个对象作为初始聚类中心；2.根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新3.对相应对象进行划分；4.重新计算每个（有变化）聚类的均值（中心对象）；5.计算标准测度函数，当满足一定条件，如函数收敛时，则算法终止；如果条件不满足则回到步骤（2），6.不断重复直到标准测度函数开始收敛为止。（一般都采用均方差作为标准测度函数。）表IRIS数据1-5051-100101-150Nox1x2x3x4Nox1x2x3x4Nox1x2x3x415.13.51.40.2517.03.24.71.41016.33.36.02.524.93.01.40.2526.43.24.51.51025.82.75.11.934.73.21.30.2536.93.14.91.51037.13.05.92.144.63.11.50.2545.52.34.01.31046.32.95.61.855.03.61.40.2556.52.84.61.51056.53.05.82.265.43.91.70.4565.72.84.51.31067.63.06.62.174.63.41.40.3576.33.34.71.61074.92.54.51.785.03.41.50.2584.92.43.31.01087.32.96.31.894.42.91.40.2596.62.94.61.31096.72.55.81.8104.93.11.50.1605.22.73.91.41107.23.66.12.5115.43.71.50.2615.02.03.51.01116.53.25.12.0124.83.41.60.2625.93.04.21.51126.42.75.31.9134.83.01.40.1636.02.24.01.01136.83.05.52.1144.33.01.10.1646.12.94.71.41145.72.55.02.0155.84.01.20.2655.62.93.91.31155.82.85.12.4165.74.41.50.4666.73.14.41.41166.43.25.32.3175.43.91.30.4675.63.04.51.51176.53.05.51.8185.13.51.40.3685.82.74.11.01187.73.86.72.2195.73.81.70.3696.22.24.51.51197.72.66.92.3205.13.81.50.3705.62.53.91.11206.02.25.01.5215.43.41.70.2715.93.24.81.81216.93.25.72.3225.13.71.50.4726.12.84.01.31225.62.84.92.0234.63.61.00.2736.32.54.91.51237.72.86.72.0245.13.31.70.5746.12.84.71.21246.32.74.91.8254.83.41.90.2756.42.94.31.31256.73.35.72.1265.03.01.60.2766.63.04.41.41267.23.26.01.8275.03.41.60.4776.82.84.81.41276.22.84.81.8285.23.51.50.2786.73.05.01.71286.13.04.91.8295.23.41.40.2796.02.94.51.51296.42.85.62.1304.73.21.60.2805.72.63.51.01307.23.05.81.6314.83.11.60.2815.52.43.81.11317.42.86.11.9325.43.41.50.4825.52.43.71.01327.93.86.42.0335.24.11.50.1835.82.73.91.21336.42.85.62.2345.54.21.40.2846.02.75.11.61346.32.85.11.5354.93.11.50.2855.43.04.51.51356.12.65.61.4365.03.21.20.2866.03.44.51.61367.73.06.12.3375.53.51.30.2876.73.14.71.51376.33.45.62.4384.93.61.40.1886.32.34.41.31386.43.15.51.8394.43.01.30.2895.63.04.11.31396.03.04.81.8405.13.41.50.2905.52.55.01.31406.93.15.42.1415.03.51.30.3915.52.64.41.21416.73.15.62.4424.52.31.30.3926.13.04.61.41426.93.15.12.3434.43.21.30.2935.82.64.01.21435.82.75.11.9445.03.51.60.6945.02.33.31.01446.83.25.92.3455.13.81.90.4955.62.74.21.31456.73.35.72.5464.83.01.40.3965.73.04.21.21466.73.05.22.3475.13.81.60.2975.72.94.21.31476.32.55.01.9484.63.21.40.2986.22.94.31.31486.53.05.22.0495.33.71.50.2995.12.53.01.11496.23.45.42.3505.03.31.40.21005.72.84.11.31505.93.05.11.8 <无> .CodeEntity .code_pieces ul.piece_anchor{width:25px;position:absolute;top:25px;left:-30px;z-index:1000;}.CodeEntity .code_pieces ul.piece_anchor li{width:25px;background: #efe;margin-bottom:2px;}.CodeEntity .code_pieces ul.piece_anchor li{border-left:3px #40AA63 solid;border-right:3px #efe solid;}.CodeEntity .code_pieces ul.piece_anchor li:hover{border-right:3px #40AA63 solid;border-left:3px #efe solid;}.CodeEntity .code_pieces ul.piece_anchor li a{color: #333;padding: 3px 10px;}.CodeEntity .code_pieces .jump_to_code{visibility:hidden;position:relative;}.CodeEntity .code_pieces .code_piece:hover .jump_to_code{visibility:visible;}.CodeEntity .code_pieces .code_piece:hover .jump_to_code a{text-decoration:none;}.CodeEntity .code_pieces h2 i{float:right;font-style:normal;font-weight:normal;}.CodeEntity .code_pieces h2 i a{font-size:9pt;background: #FFFFFF;color:#00A;padding: 2px 5px;text-decoration:none;}