<p>【电子书】数据挖掘——概念、模型、方法和算法<br/>Data Mining Concpts,Models,Methods,and Algorithms(数据挖掘——概念、模型、方法和算法)<br/>作者：（美）Mehmed Kantardzic<br/>译者：闪四清,陈茵,程雁<br/>出版社：清华大学出版社<br/><br/>作为一本教科书，本书全面讲述了数据挖掘的概念、模型、方法和算法。本书共包括13章和2个附录，全面、详细地讲述了从数据挖掘的基本概念到数据挖掘的整个过程，以及数据挖掘工具及其典型应用领域。 本收编写严谨、内容权威、结构合理、科学规范、语言流畅，特别适合作为高等院校数据挖掘课程的教科书，还适合作为数据挖掘研究人员必备的参考书。 我们被数据所包围着，这些数据是数值型或其他类型，它们都必须经过分析和处理，转换成通知、指导、回答或辅助决策和理解的信息。由于当今数据集的大小和复杂性的增加，就产生了数据挖掘这个新术语，它描述了间接的、自动化的数据分析技术，这些技术所利用的工具比分析人员过去做基本的数据分析所使用的工具更加复杂和尖端。 本书讨论了数据挖掘的原理，接着描述了一些具有代表性的艺术级的方法和算法。这些方法和算法起源于不同的学科，如统计学、机器学习、计算机图形学、数据库、信息检索、神经网络、模糊逻辑和进化计算。本书还提供了详细的算法，而且这些算法都带有必要的解释和图形示例。 本书提供了一个指南：在面对一个待挖掘的数据集(以及它们的伴随数据集)时，怎样和何时从成百上千种软件工具中选择特定的一种。本书允许分析人员用书中提供的方法和技术来创建和执行他们自己的数据挖掘实验。 本书强调选择合适的方法和数据分析软件，并根据实际情况选择相应的参数。只有在深入理解了参数的意义及其在所提供技术中的作用的情况下，才能作出这些非常重要的、定性的决策。数据挖掘是一个正在蓬勃发展的领域，本书提供了从大量可用的分析程序中进行选择所急需的指南。 <br/><br/>目&nbsp; &nbsp; 录<br/>第1章&nbsp;&nbsp;数据挖掘的概念&nbsp; &nbsp; &nbsp; &nbsp; 1<br/>1.1&nbsp;&nbsp;概述&nbsp; &nbsp; &nbsp; &nbsp; 1<br/>1.2&nbsp;&nbsp;数据挖掘的起源&nbsp; &nbsp; &nbsp; &nbsp; 3<br/>1.3&nbsp;&nbsp;数据挖掘过程&nbsp; &nbsp; &nbsp; &nbsp; 5<br/>1.3.1&nbsp;&nbsp;陈述问题和阐明假设&nbsp; &nbsp; &nbsp; &nbsp; 5<br/>1.3.2&nbsp;&nbsp;数据收集&nbsp; &nbsp; &nbsp; &nbsp; 6<br/>1.3.3&nbsp;&nbsp;数据预处理&nbsp; &nbsp; &nbsp; &nbsp; 6<br/>1.3.4&nbsp;&nbsp;模型评估&nbsp; &nbsp; &nbsp; &nbsp; 7<br/>1.3.5&nbsp;&nbsp;解释模型和得出结论&nbsp; &nbsp; &nbsp; &nbsp; 7<br/>1.4&nbsp;&nbsp;大型数据集&nbsp; &nbsp; &nbsp; &nbsp; 8<br/>1.5&nbsp;&nbsp;数据仓库&nbsp; &nbsp; &nbsp; &nbsp; 12<br/>1.6&nbsp;&nbsp;本书的结构&nbsp; &nbsp; &nbsp; &nbsp; 14<br/>1.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 15<br/>1.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 16<br/><br/>第2章&nbsp;&nbsp;数据准备&nbsp; &nbsp; &nbsp; &nbsp; 17<br/>2.1&nbsp;&nbsp;原始数据的表述&nbsp; &nbsp; &nbsp; &nbsp; 17<br/>2.2&nbsp;&nbsp;原始数据的特性&nbsp; &nbsp; &nbsp; &nbsp; 20<br/>2.3&nbsp;&nbsp;原始数据的转换&nbsp; &nbsp; &nbsp; &nbsp; 22<br/>2.4&nbsp;&nbsp;丢失数据&nbsp; &nbsp; &nbsp; &nbsp; 24<br/>2.5&nbsp;&nbsp;时间相关数据&nbsp; &nbsp; &nbsp; &nbsp; 25<br/>2.6&nbsp;&nbsp;异常点分析&nbsp; &nbsp; &nbsp; &nbsp; 29<br/>2.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 32<br/>2.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 33<br/><br/>第3章&nbsp;&nbsp;数据归约&nbsp; &nbsp; &nbsp; &nbsp; 35<br/>3.1&nbsp;&nbsp;大型数据集的维度&nbsp; &nbsp; &nbsp; &nbsp; 35<br/>3.2&nbsp;&nbsp;特征归约&nbsp; &nbsp; &nbsp; &nbsp; 37<br/>3.3&nbsp;&nbsp;特征排列的熵度量&nbsp; &nbsp; &nbsp; &nbsp; 41<br/>3.4&nbsp;&nbsp;主成分分析&nbsp; &nbsp; &nbsp; &nbsp; 43<br/>3.5&nbsp;&nbsp;值归约&nbsp; &nbsp; &nbsp; &nbsp; 45<br/>3.6&nbsp;&nbsp;特征离散化：ChiMerge技术&nbsp; &nbsp; &nbsp; &nbsp; 48<br/>3.7&nbsp;&nbsp;案例归约&nbsp; &nbsp; &nbsp; &nbsp; 51<br/>3.8&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 54<br/>3.9&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 55<br/><br/>第4章&nbsp;&nbsp;从数据中学习&nbsp; &nbsp; &nbsp; &nbsp; 57<br/>4.1&nbsp;&nbsp;机器学习&nbsp; &nbsp; &nbsp; &nbsp; 58<br/>4.2&nbsp;&nbsp;统计学习原理&nbsp; &nbsp; &nbsp; &nbsp; 62<br/>4.3&nbsp;&nbsp;学习方法的类型&nbsp; &nbsp; &nbsp; &nbsp; 67<br/>4.4&nbsp;&nbsp;常见的学习任务&nbsp; &nbsp; &nbsp; &nbsp; 68<br/>4.5&nbsp;&nbsp;模型估计&nbsp; &nbsp; &nbsp; &nbsp; 72<br/>4.6&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 76<br/>4.7&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 77<br/><br/>第5章&nbsp;&nbsp;统计方法&nbsp; &nbsp; &nbsp; &nbsp; 78<br/>5.1&nbsp;&nbsp;统计推断&nbsp; &nbsp; &nbsp; &nbsp; 78<br/>5.2&nbsp;&nbsp;评测数据集的差异&nbsp; &nbsp; &nbsp; &nbsp; 80<br/>5.3&nbsp;&nbsp;贝叶斯定理&nbsp; &nbsp; &nbsp; &nbsp; 82<br/>5.4&nbsp;&nbsp;预测回归&nbsp; &nbsp; &nbsp; &nbsp; 84<br/>5.5&nbsp;&nbsp;方差分析&nbsp; &nbsp; &nbsp; &nbsp; 89<br/>5.6&nbsp;&nbsp;对数回归&nbsp; &nbsp; &nbsp; &nbsp; 92<br/>5.7&nbsp;&nbsp;对数-线性模型&nbsp; &nbsp; &nbsp; &nbsp; 93<br/>5.8&nbsp;&nbsp;线性判别分析&nbsp; &nbsp; &nbsp; &nbsp; 96<br/>5.9&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 98<br/>5.10&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 99<br/><br/>第6章&nbsp;&nbsp;聚类分析&nbsp; &nbsp; &nbsp; &nbsp; 101<br/>6.1&nbsp;&nbsp;聚类概念&nbsp; &nbsp; &nbsp; &nbsp; 101<br/>6.2&nbsp;&nbsp;相似度的度量&nbsp; &nbsp; &nbsp; &nbsp; 104<br/>6.3&nbsp;&nbsp;凝聚层次聚类&nbsp; &nbsp; &nbsp; &nbsp; 108<br/>6.4&nbsp;&nbsp;分区聚类&nbsp; &nbsp; &nbsp; &nbsp; 112<br/>6.5&nbsp;&nbsp;增量聚类&nbsp; &nbsp; &nbsp; &nbsp; 114<br/>6.6&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 117<br/>6.7&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 119<br/><br/>第7章&nbsp;&nbsp;决策树和决策规则&nbsp; &nbsp; &nbsp; &nbsp; 120<br/>7.1&nbsp;&nbsp;决策树&nbsp; &nbsp; &nbsp; &nbsp; 121<br/>7.2&nbsp;&nbsp;C4.5算法：生成一个决策树&nbsp; &nbsp; &nbsp; &nbsp; 122<br/>7.3&nbsp;&nbsp;未知属性值&nbsp; &nbsp; &nbsp; &nbsp; 128<br/>7.4&nbsp;&nbsp;修剪决策树&nbsp; &nbsp; &nbsp; &nbsp; 132<br/>7.5&nbsp;&nbsp;C4.5算法：生成决策规则&nbsp; &nbsp; &nbsp; &nbsp; 133<br/>7.6&nbsp;&nbsp;决策树和决策规则的局限性&nbsp; &nbsp; &nbsp; &nbsp; 136<br/>7.7&nbsp;&nbsp;关联分类方法&nbsp; &nbsp; &nbsp; &nbsp; 137<br/>7.8&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 140<br/>7.9&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 142<br/><br/>第8章&nbsp;&nbsp;关联规则&nbsp; &nbsp; &nbsp; &nbsp; 144<br/>8.1&nbsp;&nbsp;购物篮分析&nbsp; &nbsp; &nbsp; &nbsp; 144<br/>8.2&nbsp;&nbsp;APRIORI 算法&nbsp; &nbsp; &nbsp; &nbsp; 146<br/>8.3&nbsp;&nbsp;从频繁项集得到关联规则&nbsp; &nbsp; &nbsp; &nbsp; 148<br/>8.4&nbsp;&nbsp;提高APRIORI算法的效率&nbsp; &nbsp; &nbsp; &nbsp; 149<br/>8.5&nbsp;&nbsp;频繁模式增长方法(FP-增长方法)&nbsp; &nbsp; &nbsp; &nbsp; 151<br/>8.6&nbsp;&nbsp;多维关联规则挖掘&nbsp; &nbsp; &nbsp; &nbsp; 153<br/>8.7&nbsp;&nbsp;WEB挖掘&nbsp; &nbsp; &nbsp; &nbsp; 154<br/>8.8&nbsp;&nbsp;HITS和LOGSOM算法&nbsp; &nbsp; &nbsp; &nbsp; 156<br/>8.9&nbsp;&nbsp;挖掘路径遍历模式&nbsp; &nbsp; &nbsp; &nbsp; 161<br/>8.10&nbsp;&nbsp;文本挖掘&nbsp; &nbsp; &nbsp; &nbsp; 164<br/>8.11&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 167<br/>8.12&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 169<br/><br/>第9章&nbsp;&nbsp;人工神经网络&nbsp; &nbsp; &nbsp; &nbsp; 171<br/>9.1&nbsp;&nbsp;人工神经元的模型&nbsp; &nbsp; &nbsp; &nbsp; 172<br/>9.2&nbsp;&nbsp;人工神经网络的结构&nbsp; &nbsp; &nbsp; &nbsp; 176<br/>9.3&nbsp;&nbsp;学习过程&nbsp; &nbsp; &nbsp; &nbsp; 177<br/>9.4&nbsp;&nbsp;学习任务&nbsp; &nbsp; &nbsp; &nbsp; 181<br/>9.5&nbsp;&nbsp;多层感知机&nbsp; &nbsp; &nbsp; &nbsp; 183<br/>9.6&nbsp;&nbsp;竞争网络和竞争学习&nbsp; &nbsp; &nbsp; &nbsp; 189<br/>9.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 193<br/>9.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 195<br/><br/>第10章&nbsp;&nbsp;遗传算法&nbsp; &nbsp; &nbsp; &nbsp; 196<br/>10.1&nbsp;&nbsp;遗传算法的基本原理&nbsp; &nbsp; &nbsp; &nbsp; 197<br/>10.2&nbsp;&nbsp;用遗传算法进行优化&nbsp; &nbsp; &nbsp; &nbsp; 198<br/>10.3&nbsp;&nbsp;遗传算法的一个简单例证&nbsp; &nbsp; &nbsp; &nbsp; 203<br/>10.4&nbsp;&nbsp;图式(SCHEMATA)&nbsp; &nbsp; &nbsp; &nbsp; 208<br/>10.5&nbsp;&nbsp;旅行推销员问题&nbsp; &nbsp; &nbsp; &nbsp; 210<br/>10.6&nbsp;&nbsp;使用遗传算法的机器学习&nbsp; &nbsp; &nbsp; &nbsp; 212<br/>10.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 216<br/>10.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 217<br/><br/>第11章&nbsp;&nbsp;模糊集和模糊逻辑&nbsp; &nbsp; &nbsp; &nbsp; 219<br/>11.1&nbsp;&nbsp;模糊集&nbsp; &nbsp; &nbsp; &nbsp; 219<br/>11.2&nbsp;&nbsp;模糊集的运算&nbsp; &nbsp; &nbsp; &nbsp; 224<br/>11.3&nbsp;&nbsp;扩展原理和模糊关系&nbsp; &nbsp; &nbsp; &nbsp; 229<br/>11.4&nbsp;&nbsp;模糊逻辑和模糊推理系统&nbsp; &nbsp; &nbsp; &nbsp; 233<br/>11.5&nbsp;&nbsp;多因子评价&nbsp; &nbsp; &nbsp; &nbsp; 237<br/>11.6&nbsp;&nbsp;从数据中提取模糊模型&nbsp; &nbsp; &nbsp; &nbsp; 239<br/>11.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 244<br/>11.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 246<br/><br/>第12章&nbsp;&nbsp;可视化方法&nbsp; &nbsp; &nbsp; &nbsp; 247<br/>12.1&nbsp;&nbsp;感知和可视化&nbsp; &nbsp; &nbsp; &nbsp; 247<br/>12.2&nbsp;&nbsp;科学可视化和信息可视化&nbsp; &nbsp; &nbsp; &nbsp; 248<br/>12.3&nbsp;&nbsp;平行坐标&nbsp; &nbsp; &nbsp; &nbsp; 253<br/>12.4&nbsp;&nbsp;放射性可视化&nbsp; &nbsp; &nbsp; &nbsp; 256<br/>12.5&nbsp;&nbsp;KOHONEN自组织映射&nbsp; &nbsp; &nbsp; &nbsp; 258<br/>12.6&nbsp;&nbsp;数据挖掘的可视化系统&nbsp; &nbsp; &nbsp; &nbsp; 259<br/>12.7&nbsp;&nbsp;复习题&nbsp; &nbsp; &nbsp; &nbsp; 263<br/>12.8&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 264<br/><br/>第13章&nbsp;&nbsp;参考书目&nbsp; &nbsp; &nbsp; &nbsp; 266<br/>附录A&nbsp;&nbsp;数据挖掘工具&nbsp; &nbsp; &nbsp; &nbsp; 281<br/>附录B&nbsp;&nbsp;数据挖掘应用&nbsp; &nbsp; &nbsp; &nbsp; 300</p><p><br/><br/><br/><br/><br/></p>

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