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<li class="chapter " data-level="1.1" data-path="../">
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前言
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第一章 绪论
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<ul class="articles">
<li class="chapter " data-level="1.2.1" data-path="../Chapter1/为什么要数据挖掘.html">
<a href="../Chapter1/为什么要数据挖掘.html">
1.1:为什么要数据挖掘
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<li class="chapter " data-level="1.2.2" data-path="../Chapter1/什么是数据挖掘.html">
<a href="../Chapter1/什么是数据挖掘.html">
1.2: 什么是数据挖掘
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<li class="chapter " data-level="1.2.3" data-path="../Chapter1/数据挖掘主要任务.html">
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1.3:数据挖掘主要任务
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<li class="chapter " data-level="1.3" data-path="../Chapter2/">
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第二章 数据探索
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<li class="chapter " data-level="1.3.1" data-path="../Chapter2/数据与属性.html">
<a href="../Chapter2/数据与属性.html">
2.1:数据与属性
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<li class="chapter " data-level="1.3.2" data-path="../Chapter2/数据的基本统计指标.html">
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2.2:数据的基本统计指标
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<li class="chapter " data-level="1.3.3" data-path="../Chapter2/数据可视化.html">
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2.3:数据可视化
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<li class="chapter " data-level="1.3.4" data-path="../Chapter2/相似性度量.html">
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2.4:相似性度量
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第三章 数据预处理
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<li class="chapter " data-level="1.4.1" data-path="../Chapter3/为什么要数据预处理.html">
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3.1:为什么要数据预处理
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<li class="chapter " data-level="1.4.2" data-path="../Chapter3/标准化.html">
<a href="../Chapter3/标准化.html">
3.2:标准化
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<li class="chapter " data-level="1.4.3" data-path="../Chapter3/非线性变换.html">
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3.3:非线性变换
</a>
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<li class="chapter " data-level="1.4.4" data-path="../Chapter3/归一化.html">
<a href="../Chapter3/归一化.html">
3.4:归一化
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<li class="chapter " data-level="1.4.5" data-path="../Chapter3/离散值编码.html">
<a href="../Chapter3/离散值编码.html">
3.5:离散值编码
</a>
</li>
<li class="chapter " data-level="1.4.6" data-path="../Chapter3/生成多项式特征.html">
<a href="../Chapter3/生成多项式特征.html">
3.6:生成多项式特征
</a>
</li>
<li class="chapter " data-level="1.4.7" data-path="../Chapter3/估算缺失值.html">
<a href="../Chapter3/估算缺失值.html">
3.7:估算缺失值
</a>
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</ul>
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第四章 k-近邻
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<li class="chapter " data-level="1.5.1" data-path="../Chapter4/k-近邻算法思想.html">
<a href="../Chapter4/k-近邻算法思想.html">
4.1:k-近邻算法思想
</a>
</li>
<li class="chapter " data-level="1.5.2" data-path="../Chapter4/k-近邻算法原理.html">
<a href="../Chapter4/k-近邻算法原理.html">
4.2:k-近邻算法原理
</a>
</li>
<li class="chapter " data-level="1.5.3" data-path="../Chapter4/k-近邻算法流程.html">
<a href="../Chapter4/k-近邻算法流程.html">
4.3:k-近邻算法流程
</a>
</li>
<li class="chapter " data-level="1.5.4" data-path="../Chapter4/动手实现k-近邻.html">
<a href="../Chapter4/动手实现k-近邻.html">
4.4:动手实现k-近邻
</a>
</li>
<li class="chapter " data-level="1.5.5" data-path="../Chapter4/实战案例.html">
<a href="../Chapter4/实战案例.html">
4.5:实战案例
</a>
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</li>
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第五章 线性回归
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<li class="chapter " data-level="1.6.1" data-path="../Chapter5/线性回归算法思想.html">
<a href="../Chapter5/线性回归算法思想.html">
5.1:线性回归算法思想
</a>
</li>
<li class="chapter " data-level="1.6.2" data-path="../Chapter5/线性回归算法原理.html">
<a href="../Chapter5/线性回归算法原理.html">
5.2:线性回归算法原理
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</li>
<li class="chapter " data-level="1.6.3" data-path="../Chapter5/线性回归算法流程.html">
<a href="../Chapter5/线性回归算法流程.html">
5.3:线性回归算法流程
</a>
</li>
<li class="chapter " data-level="1.6.4" data-path="../Chapter5/动手实现线性回归.html">
<a href="../Chapter5/动手实现线性回归.html">
5.4:动手实现线性回归
</a>
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<li class="chapter " data-level="1.6.5" data-path="../Chapter5/实战案例.html">
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5.5:实战案例
</a>
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第六章 决策树
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<li class="chapter " data-level="1.7.1" data-path="../Chapter6/决策树算法思想.html">
<a href="../Chapter6/决策树算法思想.html">
6.1:决策树算法思想
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</li>
<li class="chapter " data-level="1.7.2" data-path="../Chapter6/决策树算法原理.html">
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6.2:决策树算法原理
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<li class="chapter " data-level="1.7.3" data-path="../Chapter6/决策树算法流程.html">
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6.3:决策树算法流程
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</li>
<li class="chapter " data-level="1.7.4" data-path="../Chapter6/动手实现决策树.html">
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6.4:动手实现决策树
</a>
</li>
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6.5:实战案例
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第七章 k-均值
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<li class="chapter " data-level="1.8.1" data-path="../Chapter7/k-均值算法思想.html">
<a href="../Chapter7/k-均值算法思想.html">
7.1:k-均值算法思想
</a>
</li>
<li class="chapter " data-level="1.8.2" data-path="../Chapter7/k-均值算法原理.html">
<a href="../Chapter7/k-均值算法原理.html">
7.2:k-均值算法原理
</a>
</li>
<li class="chapter " data-level="1.8.3" data-path="../Chapter7/k-均值算法流程.html">
<a href="../Chapter7/k-均值算法流程.html">
7.3:k-均值算法流程
</a>
</li>
<li class="chapter " data-level="1.8.4" data-path="../Chapter7/动手实现k-均值.html">
<a href="../Chapter7/动手实现k-均值.html">
7.4:动手实现k-均值
</a>
</li>
<li class="chapter " data-level="1.8.5" data-path="../Chapter7/实战案例.html">
<a href="../Chapter7/实战案例.html">
7.5:实战案例
</a>
</li>
</ul>
</li>
<li class="chapter " data-level="1.9" data-path="../Chapter8/">
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第八章 Apriori
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<ul class="articles">
<li class="chapter " data-level="1.9.1" data-path="../Chapter8/Apriori算法思想.html">
<a href="../Chapter8/Apriori算法思想.html">
8.1:Apriori算法思想
</a>
</li>
<li class="chapter " data-level="1.9.2" data-path="../Chapter8/Apriori算法原理.html">
<a href="../Chapter8/Apriori算法原理.html">
8.2:Apriori算法原理
</a>
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<li class="chapter " data-level="1.9.3" data-path="../Chapter8/Apriori算法流程.html">
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8.3:Apriori算法流程
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<li class="chapter " data-level="1.9.4" data-path="../Chapter8/动手实现Apriori.html">
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8.4:动手实现Apriori
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</li>
<li class="chapter " data-level="1.9.5" data-path="../Chapter8/实战案例.html">
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8.5:实战案例
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</li>
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<li class="chapter " data-level="1.10" data-path="../Chapter9/">
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第九章 PageRank
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<li class="chapter " data-level="1.10.1" data-path="../Chapter9/PageRank算法思想.html">
<a href="../Chapter9/PageRank算法思想.html">
9.1:PageRank算法思想
</a>
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<li class="chapter " data-level="1.10.2" data-path="../Chapter9/PageRank算法原理.html">
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9.2:PageRank算法原理
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<li class="chapter " data-level="1.10.3" data-path="../Chapter9/PageRank算法流程.html">
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9.3:PageRank算法流程
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</li>
<li class="chapter " data-level="1.10.4" data-path="../Chapter9/动手实现PageRank.html">
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9.4:动手实现PageRank
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</li>
<li class="chapter " data-level="1.10.5" data-path="../Chapter9/实战案例.html">
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9.5:实战案例
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<li class="chapter " data-level="1.11" data-path="./">
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第十章 推荐系统
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<li class="chapter " data-level="1.11.1" data-path="推荐系统概述.html">
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10.1:推荐系统概述
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<li class="chapter active" data-level="1.11.2" data-path="基于矩阵分解的协同过滤算法思想.html">
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10.2:基于矩阵分解的协同过滤算法思想
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<li class="chapter " data-level="1.11.3" data-path="基于矩阵分解的协同过滤算法原理.html">
<a href="基于矩阵分解的协同过滤算法原理.html">
10.3:基于矩阵分解的协同过滤算法原理
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</li>
<li class="chapter " data-level="1.11.4" data-path="基于矩阵分解的协同过滤算法流程.html">
<a href="基于矩阵分解的协同过滤算法流程.html">
10.4:基于矩阵分解的协同过滤算法流程
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</li>
<li class="chapter " data-level="1.11.5" data-path="动手实现基于矩阵分解的协同过滤.html">
<a href="动手实现基于矩阵分解的协同过滤.html">
10.5:动手实现基于矩阵分解的协同过滤
</a>
</li>
<li class="chapter " data-level="1.11.6" data-path="实战案例.html">
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10.6:实战案例
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<p>&#x6211;&#x4EEC;&#x8BA4;&#x4E3A;&#xFF0C;&#x6709;&#x5F88;&#x591A;&#x56E0;&#x7D20;&#x4F1A;&#x5F71;&#x54CD;&#x5230;&#x7528;&#x6237;&#x7ED9;&#x7535;&#x5F71;&#x8BC4;&#x5206;&#xFF0C;&#x5982;&#x7535;&#x5F71;&#x5185;&#x5BB9;&#xFF1A;&#x611F;&#x60C5;&#x620F;&#xFF0C;&#x6050;&#x6016;&#x5143;&#x7D20;&#xFF0C;&#x52A8;&#x4F5C;&#x6210;&#x5206;&#xFF0C;&#x63A8;&#x7406;&#x60AC;&#x7591;&#x7B49;&#x7B49;&#x3002;&#x5047;&#x8BBE;&#x6211;&#x4EEC;&#x73B0;&#x5728;&#x60F3;&#x9884;&#x6D4B;&#x7528;&#x6237;<code>2</code>&#x5BF9;&#x7535;&#x5F71;<code>2</code>&#x7684;&#x8BC4;&#x5206;&#xFF0C;&#x7528;&#x6237;<code>2</code>&#x4ED6;&#x5F88;&#x559C;&#x6B22;&#x770B;&#x52A8;&#x4F5C;&#x7247;&#x4E0E;&#x63A8;&#x7406;&#x60AC;&#x7591;&#xFF0C;&#x4E0D;&#x559C;&#x6B22;&#x770B;&#x611F;&#x60C5;&#x620F;&#x4E0E;&#x6050;&#x6016;&#x7684;&#x5143;&#x7D20;&#xFF0C;&#x800C;&#x7535;&#x5F71;2&#x53EA;&#x6709;&#x5C11;&#x91CF;&#x7684;&#x611F;&#x60C5;&#x620F;&#x4E0E;&#x6050;&#x6016;&#x5143;&#x7D20;&#xFF0C;&#x5927;&#x90E8;&#x5206;&#x90FD;&#x662F;&#x52A8;&#x4F5C;&#x4E0E;&#x63A8;&#x7406;&#x7684;&#x5267;&#x60C5;&#xFF0C;&#x5219;&#x7528;&#x6237;<code>2</code>&#x5BF9;&#x7535;&#x5F71;<code>2</code>&#x8BC4;&#x5206;&#x53EF;&#x80FD;&#x5F88;&#x9AD8;&#xFF0C;&#x6BD4;&#x5982;<code>5</code>&#x5206;&#x3002;</p>
<p>&#x57FA;&#x4E8E;&#x4E0A;&#x9762;&#x7684;&#x8BBE;&#x60F3;&#xFF0C;&#x6211;&#x4EEC;&#x53EA;&#x8981;&#x77E5;&#x9053;&#x6240;&#x6709;&#x7528;&#x6237;&#x5BF9;&#x7535;&#x5F71;&#x5185;&#x5BB9;&#x5404;&#x79CD;&#x5143;&#x7D20;&#x559C;&#x6B22;&#x7A0B;&#x5EA6;&#x4E0E;&#x6240;&#x6709;&#x7535;&#x5F71;&#x5185;&#x5BB9;&#x7684;&#x6210;&#x5206;&#xFF0C;&#x5C31;&#x80FD;&#x9884;&#x6D4B;&#x51FA;&#x6240;&#x6709;&#x7528;&#x6237;&#x5BF9;&#x6240;&#x6709;&#x7535;&#x5F71;&#x7684;&#x8BC4;&#x5206;&#x4E86;&#x3002;
&#x82E5;&#x53EA;&#x8003;&#x8651;&#x4E24;&#x79CD;&#x5143;&#x7D20;&#x5219;&#x7528;&#x6237;&#x559C;&#x597D;&#x8868;&#x4E0E;&#x7535;&#x5F71;&#x5185;&#x5BB9;&#x8868;&#x5982;&#x4E0B;&#xFF1A;</p>
<p>&#x7528;&#x6237;&#x559C;&#x597D;&#x8868;<code>x</code>&#xFF1A;</p>
<table>
<thead>
<tr>
<th>x</th>
<th>&#x56E0;&#x7D20;1</th>
<th>&#x56E0;&#x7D20;2</th>
</tr>
</thead>
<tbody>
<tr>
<td>&#x7528;&#x6237;1</td>
<td>5</td>
<td>0</td>
</tr>
<tr>
<td>&#x7528;&#x6237;2</td>
<td>5</td>
<td>0</td>
</tr>
<tr>
<td>&#x7528;&#x6237;3</td>
<td>0</td>
<td>5</td>
</tr>
<tr>
<td>&#x7528;&#x6237;4</td>
<td>0</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>&#x503C;&#x8D8A;&#x5927;&#x4EE3;&#x8868;&#x7528;&#x6237;&#x8D8A;&#x559C;&#x6B22;&#x67D0;&#x79CD;&#x5143;&#x7D20;&#x3002;</p>
<p>&#x7535;&#x5F71;&#x5185;&#x5BB9;&#x8868;&#xFF1A;<code>w</code>:</p>
<table>
<thead>
<tr>
<th>w</th>
<th>&#x7535;&#x5F71;1</th>
<th>&#x7535;&#x5F71;2</th>
<th>&#x7535;&#x5F71;3</th>
<th>&#x7535;&#x5F71;4</th>
<th>&#x7535;&#x5F71;5</th>
</tr>
</thead>
<tbody>
<tr>
<td>&#x56E0;&#x7D20;1</td>
<td>0.9</td>
<td>1.0</td>
<td>0.99</td>
<td>0.1</td>
<td>0</td>
</tr>
<tr>
<td>&#x56E0;&#x7D20;2</td>
<td>0</td>
<td>0.01</td>
<td>0</td>
<td>1.0</td>
<td>0.9</td>
</tr>
</tbody>
</table>
<p>&#x503C;&#x8D8A;&#x5927;&#x4EE3;&#x8868;&#x7535;&#x5F71;&#x4E2D;&#x67D0;&#x5143;&#x7D20;&#x5185;&#x5BB9;&#x8D8A;&#x591A;&#x3002;</p>
<p>&#x7528;&#x6237;<code>2</code>&#x5BF9;&#x7535;&#x5F71;<code>2</code>&#x8BC4;&#x5206;&#x4E3A;&#xFF1A;<span class="katex"><span class="katex-mathml"><math><semantics><mrow><mn>5</mn><mo>&#xD7;</mo><mn>1</mn><mi mathvariant="normal">.</mi><mn>0</mn><mo>+</mo><mn>0</mn><mo>&#xD7;</mo><mn>0</mn><mi mathvariant="normal">.</mi><mn>0</mn><mn>1</mn><mo>=</mo><mn>5</mn><mi mathvariant="normal">.</mi><mn>0</mn></mrow><annotation encoding="application/x-tex">5\times 1.0 +0\times 0.01 = 5.0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.64444em;"></span><span class="strut bottom" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="base textstyle uncramped"><span class="mord mathrm">5</span><span class="mbin">&#xD7;</span><span class="mord mathrm">1</span><span class="mord mathrm">.</span><span class="mord mathrm">0</span><span class="mbin">+</span><span class="mord mathrm">0</span><span class="mbin">&#xD7;</span><span class="mord mathrm">0</span><span class="mord mathrm">.</span><span class="mord mathrm">0</span><span class="mord mathrm">1</span><span class="mrel">=</span><span class="mord mathrm">5</span><span class="mord mathrm">.</span><span class="mord mathrm">0</span></span></span></span></p>
<p>&#x5BF9;&#x4E8E;&#x6240;&#x6709;&#x7528;&#x6237;&#xFF0C;&#x6211;&#x4EEC;&#x53EF;&#x4EE5;&#x5C06;&#x77E9;&#x9635;<code>x</code>&#x4E0E;&#x77E9;&#x9635;<code>w</code>&#x76F8;&#x4E58;&#xFF0C;&#x5F97;&#x5230;&#x6240;&#x6709;&#x7528;&#x6237;&#x5BF9;&#x6240;&#x6709;&#x7535;&#x5F71;&#x7684;&#x9884;&#x6D4B;&#x8BC4;&#x5206;&#x5982;&#x4E0B;&#x8868;&#xFF1A;</p>
<table>
<thead>
<tr>
<th>xw</th>
<th>&#x7535;&#x5F71;1</th>
<th>&#x7535;&#x5F71;2</th>
<th>&#x7535;&#x5F71;3</th>
<th>&#x7535;&#x5F71;4</th>
<th>&#x7535;&#x5F71;5</th>
</tr>
</thead>
<tbody>
<tr>
<td>&#x7528;&#x6237;1</td>
<td>4.5</td>
<td>5.0</td>
<td>4.95</td>
<td>0.5</td>
<td>0</td>
</tr>
<tr>
<td>&#x7528;&#x6237;2</td>
<td>4.5</td>
<td>5.0</td>
<td>4.95</td>
<td>0.5</td>
<td>0</td>
</tr>
<tr>
<td>&#x7528;&#x6237;3</td>
<td>0</td>
<td>0.05</td>
<td>0</td>
<td>5</td>
<td>4.5</td>
</tr>
<tr>
<td>&#x7528;&#x6237;4</td>
<td>0</td>
<td>0.05</td>
<td>0</td>
<td>5</td>
<td>4.5</td>
</tr>
</tbody>
</table>
<p>&#x5047;&#x8BBE;&#x7535;&#x5F71;&#x8BC4;&#x5206;&#x8868;<code>y</code>&#xFF08;&#x4E3A;<code>m</code>&#x884C;<code>n</code>&#x5217;&#x7684;&#x77E9;&#x9635;&#xFF09;,&#x6211;&#x4EEC;&#x8003;&#x8651;<code>d</code>&#x79CD;&#x5143;&#x7D20;&#xFF0C;&#x5219;&#x7535;&#x5F71;&#x8BC4;&#x5206;&#x8868;&#x53EF;&#x4EE5;&#x5206;&#x89E3;&#x4E3A;&#x7528;&#x6237;&#x559C;&#x597D;&#x8868;<code>x</code>&#xFF08;&#x4E3A;<code>m</code>&#x884C;<code>d</code>&#x5217;&#x7684;&#x77E9;&#x9635;&#xFF09;&#xFF0C;&#x4E0E;&#x7535;&#x5F71;&#x5185;&#x5BB9;&#x8868;<code>w</code>&#xFF08;&#x4E3A;<code>d</code>&#x884C;<code>n</code>&#x5217;&#x7684;&#x77E9;&#x9635;&#xFF09;&#x3002;&#x5176;&#x4E2D;<code>d</code>&#x4E3A;&#x8D85;&#x53C2;&#x6570;&#xFF0C;&#x5927;&#x5C0F;&#x7531;&#x6211;&#x4EEC;&#x81EA;&#x5DF1;&#x5B9A;&#x3002;</p>
<p>&#x57FA;&#x4E8E;&#x77E9;&#x9635;&#x5206;&#x89E3;&#x7684;&#x534F;&#x540C;&#x8FC7;&#x6EE4;&#x7B97;&#x6CD5;&#x601D;&#x60F3;&#x4E3A;&#xFF1A;<strong>&#x4E00;&#x4E2A;&#x7528;&#x6237;&#x8BC4;&#x5206;&#x77E9;&#x9635;&#x53EF;&#x4EE5;&#x5206;&#x89E3;&#x4E3A;&#x4E00;&#x4E2A;&#x7528;&#x6237;&#x559C;&#x597D;&#x77E9;&#x9635;&#x4E0E;&#x5185;&#x5BB9;&#x77E9;&#x9635;&#xFF0C;&#x6211;&#x4EEC;&#x53EA;&#x8981;&#x80FD;&#x627E;&#x51FA;&#x6B63;&#x786E;&#x7684;&#x7528;&#x6237;&#x559C;&#x597D;&#x77E9;&#x9635;&#x53C2;&#x6570;&#x4E0E;&#x5185;&#x5BB9;&#x77E9;&#x9635;&#x53C2;&#x6570;&#xFF08;&#x5373;&#x8868;&#x5185;&#x7684;&#x503C;&#xFF09;&#xFF0C;&#x5C31;&#x80FD;&#x5BF9;&#x7528;&#x6237;&#x8BC4;&#x5206;&#x8FDB;&#x884C;&#x9884;&#x6D4B;&#xFF0C;&#x518D;&#x6839;&#x636E;&#x9884;&#x6D4B;&#x7ED3;&#x679C;&#x5BF9;&#x7528;&#x6237;&#x8FDB;&#x884C;&#x63A8;&#x8350;&#x3002;</strong></p>
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