大小写的希腊字母的LaTex写法
离散化Discretization
unsupervised discretization
- equal width
- equal frequency
- clustering
supervised discretization – entropy-based
基于熵
e n t r o p y ( S ) = − ∑ i P i ⋅ l o g 2 P i entropy(S)=-\sum_iP_i \cdot log_2 P_i entropy(S)=−i∑Pi⋅log2Pi
Normalization and Standardization
- performe for each attribute
- normalization
x ′ = x − m i n ( x ) m a x ( x ) − m i n ( x ) x'=\frac{x-min(x)}{max(x)-min(x)} x′=max(x)−min(x)x−min(x) - standardization
x ′ = x − μ ( x ) σ ( x ) x'=\frac{x-\mu(x)}{\sigma(x)} x′=σ(x)x−μ(x)
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