From Data Mining and Analysis

Main: Errata

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Chapter 1: Data Mining and Analysis

Chapter 2: Numeric Attributes

$$\sum_{i=1}^n (x_i - \mu)^2 = n(\hat{\mu} - \mu)^2 + \sum_{i=1}^n (x_i - \hat{\mu})^2$$

Chapter 3: Categorical Attributes

Chapter 4: Graph Data

Chapter 5: Kernel Methods

$$\mathbf{\mu}_\phi = \sum_{i=1}^5 \phi(\mathbf{x}_i) = \sum_{i=1}^5 \mathbf{x}_i$$

should be

$$\mathbf{\mu}_\phi = \frac{1}{5}\sum_{i=1}^5 \phi(\mathbf{x}_i) = \frac{1}{5} \sum_{i=1}^5 \mathbf{x}_i$$

Chapter 6: High-dimensional Data

Chapter 7: Dimensionality Reduction

Chapter 8: Itemset Mining

Chapter 9: Summarizing Itemsets

$$ \textbf{Upper Bounds} \bigl(\bigl|X\setminus Y\bigr| \text{is odd} \bigr): sup(X) \leq\sum_{Y \subseteq W \subset X} -1^{\bigl(\bigl|X\setminus W\bigr|+1\bigr)} sup(W) $$ $$ \textbf{Lower Bounds} \bigl(\bigl|X\setminus Y\bigr| \text{is even}\bigr): sup(X) \geq\sum_{Y \subseteq W \subset X} -1^{\bigl(\bigl|X\setminus W\bigr|+1\bigr)} sup(W) $$

$$\mathit{IE}(Y) = \sum_{Y \subseteq W \subset X}\, -1^{\bigl(\bigl|X\setminus W\bigr|+1\bigr)} \cdot sup(W)$$

Chapter 10: Sequence Mining

Chapter 11: Graph Pattern Mining

Chapter 12: Pattern and Rule Assessment

Chapter 13: Representative-based Clustering

Chapter 14: Hierarchical Clustering

Chapter 15: Density-based Clustering

Chapter 16: Spectral and Graph Clustering

$$ p_{rs} = \frac{d_r}{2m}\frac{d_s}{2m} = \frac{d_r d_s}{4m^2} $$

Chaper 17: Clustering Validation

Chapter 18: Probabilistic Classification

Chapter 20: Linear Discriminant Analysis

$$\mathbf{\mu}_1 = \pmatrix{5.01\\3.42}^T \qquad \mathbf{\mu}_2 = \pmatrix{6.26\\2.87}^T \qquad \mathbf{\mu}_1 - \mathbf{\mu}_2= \pmatrix{-1.256\\0.546}^T$$

should be

$$\mathbf{\mu}_1 = \pmatrix{5.01\\3.42} \qquad \mathbf{\mu}_2 = \pmatrix{6.26\\2.87} \qquad \mathbf{\mu}_1 - \mathbf{\mu}_2 = \pmatrix{-1.256\\0.546}$$

Chapter 21: Support Vector Machines

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