├── CC.csv ├── CFSFDP.py ├── LICENSE ├── Readme.md ├── ResultCenter.csv ├── data.csv └── readme.markdown /CC.csv: -------------------------------------------------------------------------------- 1 | ,lat,lon,Gamma 2 | 0,351689.7463,3647613.725,1.0 3 | 1,351466.7963,3647874.996,0.4111117743984921 4 | 2,351461.9095,3647601.086,0.3424317350941342 5 | 3,351473.6657,3648163.106,0.30503210169555645 6 | 4,351572.8678,3647734.094,0.155369837694129 7 | 5,351496.2542,3647961.1,0.1365657501792723 8 | 6,351499.0288,3647806.669,0.11337214800464283 9 | 7,351466.3989,3647836.459,0.05783409104478581 10 | 8,351468.3046,3647836.42,0.0028604106079968077 11 | -------------------------------------------------------------------------------- /CFSFDP.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # Clustering by fast search and find of density peaks 3 | import numpy as np 4 | import numpy 5 | from scipy.spatial.distance import pdist 6 | from scipy.spatial.distance import squareform 7 | import pandas as pd 8 | import csv 9 | # 确定每点的最终分类 10 | # densitySortArr密度从小到大的索引排序 11 | # closestNodeIdArr是比自己密度大，距离自己最近点的id 12 | # n代表一共有多少条数据 13 | def extract_cluster(densitySortArr,closestNodeIdArr, classNum,gamma): 14 | n=densitySortArr.shape[0] 15 | # 初始化每一个点的类别 16 | labels=np.full((n,),-1) 17 | corePoints = np.argsort(-gamma)[: classNum] # 选择 18 | # 将选择的聚类中心赋予类别 19 | labels[corePoints]=range(len(corePoints)) 20 | # 将ndarrar数组转为list集合 21 | densitySortList=densitySortArr.tolist() 22 | # 将集合元素反转，即密度从大到小的排序索引 23 | densitySortList.reverse() 24 | # 循环赋值每一个元素的label 25 | for nodeId in densitySortList: 26 | if(labels[nodeId]==-1): 27 | # 如果nodeId节点没有类别 28 | # 首先获得closestNodeIdArr[nodeId] 比自己密度大，且距离自己最近的点的索引 29 | # 将比自己密度大，且距离自己最近的点的类别复制给nodeId 30 | labels[nodeId]=labels[closestNodeIdArr[nodeId]] 31 | return corePoints,labels 32 | 33 | def CFSFDP(data,dc): 34 | n,m=data.shape 35 | # 制作任意两点之间的距离矩阵 36 | disMat = squareform(pdist(data,metric='euclidean')) 37 | # 计算每一个点的密度（在dc的圆中包含几个点） 38 | densityArr = np.where(disMat < dc, 1, 0).sum(axis=1) 39 | # 将数据点按照密度大小进行排序（从小到大） 40 | densitySortArr=np.argsort(densityArr) 41 | # 初始化，比自己密度大的且最近的距离 42 | closestDisOverSelfDensity = np.zeros((n,)) 43 | # 初始化，比自己密度大的且最近的距离对应的节点id 44 | closestNodeIdArr = np.zeros((n,), dtype=np.int32) 45 | # 从密度最小的点开始遍历 46 | for index,nodeId in enumerate(densitySortArr): 47 | # 点密度大于当前点的点集合 48 | nodeIdArr = densitySortArr[index+1:] 49 | # 如果不是密度最大的点 50 | if nodeIdArr.size != 0: 51 | # 计算比自己密度大的点距离nodeId的距离集合 52 | largerDistArr = disMat[nodeId][nodeIdArr] 53 | # 寻找到比自己密度大，且最小的距离节点 54 | closestDisOverSelfDensity[nodeId] = np.min(largerDistArr) 55 | # 寻找到最小值的索引，索引实在largerdist里面的索引（确保是比nodeId）节点大 56 | # 如果存在多个最近的节点，取第一个 57 | # 注意，这里是largerDistArr里面的索引 58 | min_distance_index = np.argwhere(largerDistArr == closestDisOverSelfDensity[nodeId])[0][0] 59 | # 获得整个数据中的索引值 60 | closestNodeIdArr[nodeId] = nodeIdArr[min_distance_index] 61 | else: 62 | # 如果是密度最大的点，距离设置为最大，且其对应的ID设置为本身 63 | closestDisOverSelfDensity[nodeId] = np.max(closestDisOverSelfDensity) 64 | closestNodeIdArr[nodeId] = nodeId 65 | # 由于密度和最短距离两个属性的数量级可能不一样，分别对两者做归一化使结果更平滑 66 | normal_den = (densityArr - np.min(densityArr)) / (np.max(densityArr) - np.min(densityArr)) 67 | normal_dis = (closestDisOverSelfDensity - np.min(closestDisOverSelfDensity)) / ( 68 | np.max(closestDisOverSelfDensity) - np.min(closestDisOverSelfDensity)) 69 | gamma = normal_den * normal_dis 70 | 71 | 72 | return densityArr,densitySortArr,closestDisOverSelfDensity,closestNodeIdArr,gamma 73 | 74 | 75 | 76 | 77 | if __name__ == '__main__': 78 | data = np.loadtxt("C:/Users/Desktop/Data/data.csv", delimiter=",") 79 | # 执行聚类算法 80 | densityArr,densitySortArr,closestDisOverSelfDensity,closestNodeIdArr,gamma= CFSFDP(data,2) 81 | # 根据决策图提取类别 82 | #自动确定聚类中心数目 83 | G = [] 84 | G = numpy.matrix.tolist(gamma) 85 | G.sort(reverse = True) 86 | K = [] 87 | for i in range(len(G)-1): 88 | k =G[i] - G[i+1] 89 | K.append(k) 90 | ksum = 0 91 | for i in range(len(K)): 92 | ksum = ksum+K[i] 93 | R = ksum/len(K) 94 | Result = 1 95 | for i in range(len(K)): 96 | if K[i] > R: 97 | Result = Result + 1 98 | # 聚类中心数目传值 99 | classNum = Result 100 | #导出聚类中心坐标 101 | corePoints,labels = extract_cluster(densitySortArr, closestNodeIdArr, classNum, gamma) 102 | X = data[corePoints,0] 103 | Y = data[corePoints,1] 104 | CC = [] 105 | for i in range(len(X)): 106 | CC.append([X[i],Y[i]]) 107 | name1 = ['lat', 'lon'] 108 | S = pd.DataFrame(columns=name1, data=CC) 109 | S.to_csv('C:/Users/Desktop/CC.csv', encoding='utf-8') 110 | #******** 111 | #分类结果导出 112 | M = [] 113 | R = [] 114 | L = [] 115 | for n in corePoints: 116 | M.append(n) 117 | with open("C:/Users/Desktop/Data/data.csv", 'r',encoding='utf-8') as csvfile: 118 | reader = csv.reader(csvfile) 119 | rows = [row for row in reader] 120 | for i in labels: 121 | L.append((i)) 122 | for x in range(0,len(rows)): 123 | R.append([rows[x][0],rows[x][1],L[x]]) 124 | name = ['lat', 'lon', 'code' ] 125 | SortR = pd.DataFrame(columns=name, data=R) 126 | SortR.to_csv('C:/Users/Desktop/ResultCenter.csv', encoding='utf-8') 127 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 |
2 | By YunFan 3 |

改进的基于密度峰值的快速聚类算法CFSFDP

4 |

——自动确定聚类中心

5 |

运行环境:

6 | 7 | python版本：python-3.7.4-amd64.exe 8 |
9 | numpy版本：numpy-1.16.5+mkl-cp37-cp37m-win_amd64.whl 10 | 11 |

示例数据：

12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 |
XY
351471.70093647557.073
351521.0373 3647559.228
26 | 27 | X,Y：POI点的投影坐标 28 | 29 |

结果文件：

30 | 31 | ResultCenter.csv：聚类结果
CC.csv：聚类中心 32 | 33 | -------------------------------------------------------------------------------- /ResultCenter.csv: -------------------------------------------------------------------------------- 1 | ,lat,lon,code 2 | 0,351471.7009,3647557.073,2 3 | 1,351521.0373,3647559.228,2 4 | 2,351574.1303,3647569.664,2 5 | 3,351473.965,3647566.424,2 6 | 4,351488.1055,3647578.161,2 7 | 5,351505.3291,3647594.537,2 8 | 6,351461.9095,3647601.086,2 9 | 7,351461.9095,3647601.086,2 10 | 8,351689.7463,3647613.725,0 11 | 9,351689.7463,3647613.725,0 12 | 10,351689.7463,3647613.725,0 13 | 11,351490.9449,3647609.903,2 14 | 12,351506.7587,3647611.312,2 15 | 13,351505.233,3647615.545,2 16 | 14,351460.5434,3647615.153,2 17 | 15,351420.9161,3647629.08,2 18 | 16,351479.5162,3647637.811,4 19 | 17,351475.8905,3647642.413,4 20 | 18,351508.3196,3647645.602,4 21 | 19,351496.2559,3647650.327,4 22 | 20,351475.1249,3647655.036,4 23 | 21,351506.4366,3647658.971,4 24 | 22,351468.9331,3647660.329,4 25 | 23,351480.741,3647662.146,4 26 | 24,351470.0089,3647667.601,4 27 | 25,351571.3989,3647674.277,4 28 | 26,351480.5718,3647678.068,4 29 | 27,351546.5457,3647699.966,4 30 | 28,351461.1399,3647697.304,4 31 | 29,351572.8678,3647734.094,4 32 | 30,351572.8678,3647734.094,4 33 | 31,351311.3342,3647762.391,7 34 | 32,351706.1105,3647784.155,4 35 | 33,351497.5279,3647777.803,6 36 | 34,351307.0222,3647772.954,7 37 | 35,351493.4074,3647781.144,6 38 | 36,351303.4878,3647786.824,7 39 | 37,351499.0288,3647806.669,6 40 | 38,351499.1878,3647807.24,6 41 | 39,351509.1165,3647813.05,6 42 | 40,351517.1194,3647817.091,6 43 | 41,351473.7292,3647815.959,8 44 | 42,351393.9359,3647815.89,7 45 | 43,351497.9365,3647823.006,6 46 | 44,351467.0231,3647822.814,8 47 | 45,351495.0901,3647823.912,6 48 | 46,351466.8534,3647829.473,8 49 | 47,351468.3046,3647836.42,8 50 | 48,351466.3989,3647836.459,7 51 | 49,351467.2205,3647838.637,7 52 | 50,351555.5774,3647842.319,6 53 | 51,351454.7264,3647842.78,7 54 | 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351461.9095,3647601.086 9 | 351689.7463,3647613.725 10 | 351689.7463,3647613.725 11 | 351689.7463,3647613.725 12 | 351490.9449,3647609.903 13 | 351506.7587,3647611.312 14 | 351505.233,3647615.545 15 | 351460.5434,3647615.153 16 | 351420.9161,3647629.08 17 | 351479.5162,3647637.811 18 | 351475.8905,3647642.413 19 | 351508.3196,3647645.602 20 | 351496.2559,3647650.327 21 | 351475.1249,3647655.036 22 | 351506.4366,3647658.971 23 | 351468.9331,3647660.329 24 | 351480.741,3647662.146 25 | 351470.0089,3647667.601 26 | 351571.3989,3647674.277 27 | 351480.5718,3647678.068 28 | 351546.5457,3647699.966 29 | 351461.1399,3647697.304 30 | 351572.8678,3647734.094 31 | 351572.8678,3647734.094 32 | 351311.3342,3647762.391 33 | 351706.1105,3647784.155 34 | 351497.5279,3647777.803 35 | 351307.0222,3647772.954 36 | 351493.4074,3647781.144 37 | 351303.4878,3647786.824 38 | 351499.0288,3647806.669 39 | 351499.1878,3647807.24 40 | 351509.1165,3647813.05 41 | 351517.1194,3647817.091 42 | 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77 | 351495.2913,3647957.899 78 | 351496.2542,3647961.1 79 | 351370.9283,3647956.764 80 | 351495.9517,3647961.878 81 | 351473.9467,3647961.131 82 | 351463.9422,3647978.137 83 | 351492.2943,3647992.801 84 | 351473.4212,3648004.716 85 | 351494.6447,3648011.533 86 | 351488.8555,3648025.089 87 | 351491.6489,3648025.537 88 | 351500.6687,3648054.583 89 | 351581.1184,3648065.637 90 | 351567.1516,3648068.028 91 | 351550.3535,3648112.671 92 | 351490.5049,3648110.444 93 | 351559.1158,3648115.838 94 | 351336.8906,3648120.027 95 | 351508.3573,3648140.852 96 | 351900.6833,3648159.931 97 | 351473.6657,3648163.106 98 | 351473.6657,3648163.106 99 | 351476.6805,3648176.441 100 | -------------------------------------------------------------------------------- /readme.markdown: -------------------------------------------------------------------------------- 1 | ![](https://i.loli.net/2020/11/10/dshybin4t9DepvY.png) 2 | # 改进的基于密度峰值的快速聚类算法CFSFDP# 3 | #——自动确定聚类中心 # 4 | ## 运行环境: ## 5 | 6 | python版本：python-3.7.4-amd64.exe 7 | numpy版本：numpy-1.16.5+mkl-cp37-cp37m-win_amd64.whl 8 | 9 | ## 示例数据： ## 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 |
XY
351471.70093647557.073
351521.0373 3647559.228
24 | 25 | X,Y：POI点的投影坐标 26 | ## 结果文件： ## 27 | ResultCenter.csv：聚类结果 28 | CC.csv：聚类中心 29 | 30 | --------------------------------------------------------------------------------