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群众的力量是伟大的-随机森林
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物以类聚人以群分-kMeans
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<!-- Title -->
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<h1>
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<a href="." >物以类聚人以群分-kMeans</a>
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<h1 id="物以类聚人以群分-k-means">物以类聚人以群分-k Means</h1>
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<p><strong>k Means</strong>是属于机器学习里面的非监督学习，通常是大家接触到的第一个聚类算法，其原理非常简单，是一种典型的基于<strong>距离</strong>的聚类算法。<strong>距离</strong>指的是每个样本到质心的距离。那么，这里所说的质心是什么呢？</p>
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<p>其实，质心指的是样本每个特征的均值所构成的一个坐标。举个例子：假如有两个数据 <script type="math/tex; ">(1,1)</script> 和<script type="math/tex; ">(2,2)</script> 则这两个样本的质心为 <script type="math/tex; ">(1.5,1.5)</script>。</p>
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<p>同样的，如果一份数据有 <script type="math/tex; ">m</script> 个样本，每个样本有 <script type="math/tex; ">n</script> 个特征，用 <script type="math/tex; ">x_i^j</script> 来表示第 <script type="math/tex; ">j</script> 个样本的第 <script type="math/tex; ">i</script> 个特征，则它们的质心为：<script type="math/tex; ">Cmass=(\frac{\sum_{j=1}^mx_1^j}{m},\frac{\sum_{j=1}^mx_2^j}{m},...,\frac{\sum_{j=1}^mx_n^j}{m})</script>。</p>
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<p>知道什么是质心后，就可以看看<strong>k Means算法</strong>的流程了。</p>
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<h2 id="k-means算法流程">k Means算法流程</h2>
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<p>使用<strong>k Means</strong>来聚类时需要首先定义参数<strong>k</strong>，<strong>k</strong>的意思是我想将数据聚成几个类别。假设<strong>k=3</strong>，就是将数据划分成<strong>3</strong>个类别。接下来就可以开始<strong>k Means</strong>算法的流程了，流程如下：</p>
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<p><code>1.</code>随机初始<strong>k</strong>个样本，作为类别中心。
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<code>2.</code>对每个样本将其标记为距离类别中心最近的类别。
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<code>3.</code>将每个类别的质心更新为新的类别中心。
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<code>4.</code>重复步骤<code>2</code>、<code>3</code>，直到类别中心的变化小于阈值。</p>
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<p>过程示意图如下（其中 X 表示类别的中心，数据点的颜色代表不同的类别）：</p>
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<p><img src="img/29.gif" alt=""></p>
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