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《面向程序员的数据挖掘指南》源码

Home Page: http://dataminingguide.books.yourtion.com

Python 100.00%

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《面向程序员的数据挖掘指南》 源码

目录

讲述什么是数据挖掘,它所能解决的问题的是什么,以及在阅读完本书后,你可以做些什么。

介绍协同过滤,基本的距离算法,包括曼哈顿距离、欧几里得距离、闵科夫斯基距离、皮尔森相关系数。使用Python实现一个基本的推荐算法。

这章开始讨论可供选择的用户评价体系。用户能够显示地給于评价(好、差、五星评价等),或者隐式地給于评价——如果用户在亚马逊购买了一个MP3,我们则认为他是“喜欢”这件商品的。

上一章中我们使用用户对商品的评价来进行推荐,这一章我们会使用商品本身的特性来进行推荐。这种算法在潘多拉等网站中采用。

本章会讨论如何评价分类器的效果,方法包括十折交叉验证、留一法、以及Kappa检验等,同时还会引入kNN算法。

我们会在这章探索朴素贝叶斯分类算法,使用概率密度函数来处理数值型数据。

这一章我们会尝试使用朴素贝叶斯算法来对非结构化文本进行分类。我们是否能够判断出Twitter上的一片影评是正面评价还是负面的呢?

我们会讨论层次聚类和kmeans聚类。

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