数据挖掘中的宁滨
数据宁滨，分时段是一种数据预处理方法，用于最小化小观测误差的影响。原始数据值被划分为称为箱的小间隔，然后它们被为该箱计算的一般值代替。这将对输入数据产生平滑效果，并且还可能减少在小数据集的情况下过度拟合的机会 有两种方法将数据划分为面元:
- 等频宁滨:仓具有相同的频率。
- 等宽宁滨:仓具有等宽，每个仓的范围定义为[min + w]，[min + 2w] …。[最小+西北]其中 w =(最大–最小)/(箱数)。
等频:
Input:[5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
Output:
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]
等宽:
Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]
代码:比宁技术实现:
巴拉圭
# equal frequency
def equifreq(arr1, m):
a = len(arr1)
n = int(a / m)
for i in range(0, m):
arr = []
for j in range(i * n, (i + 1) * n):
if j >= a:
break
arr = arr + [arr1[j]]
print(arr)
# equal width
def equiwidth(arr1, m):
a = len(arr1)
w = int((max(arr1) - min(arr1)) / m)
min1 = min(arr1)
arr = []
for i in range(0, m + 1):
arr = arr + [min1 + w * i]
arri=[]
for i in range(0, m):
temp = []
for j in arr1:
if j >= arr[i] and j <= arr[i+1]:
temp += [j]
arri += [temp]
print(arri)
# data to be binned
data = [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
# no of bins
m = 3
print("equal frequency binning")
equifreq(data, m)
print("\n\nequal width binning")
equiwidth(data, 3)
输出:
equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]
equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]]
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