Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining.

Ethash Ethash的C / C ++实现–以太坊工作量证明算法 目录 安装 使用CMake从源代码构建。 mkdir build cd build cmake .. cmake --build . 用法 有关导出的功能和文档的列表，请参见 。 测试向量 最佳化 本节描述了与有关的优化，修改和调整。 该库包含一组微基准。 构建并运行bench工具。 种子哈希是动态计算的。 种子哈希是keccak256哈希序列的应用次数。 与构建轻型缓存所需的时间相比，计算种子哈希所需的时间可以忽略不计。 计算时间段10000的种子哈希大约需要5毫秒，构建时间段1的光缓存大约需要500毫秒。 数据

rplos：PLoS Journals API的R客户端

AirBnB-Data-Analysis：一个数据分析笔记本以及一个针对希腊雅典的Airbnb列表的推荐系统

算法与解决方案 它旨在在我知道或正在学习的文档和示例项目中创建基本文档和解决问题的方法。 可用标题 仿射密码（线性加密）算法 选择排序 先验算法 K-NN算法 贝叶斯分类器算法 背包算法 每日移动平均 二元搜寻 最长公共子序列 标记和扫描算法（垃圾收集方法） 准备标题 尝试方法 -（土耳其文）

这是压缩文件的第二部分。Introduction to Data Mining 数据挖掘导论 包含完整地中文版、英文版 PPT 等习题答案均在压缩包中，一共有两个解压文件。两个文件下载完成后才能解压缩。

Data Mining: The Textbook By 作者: Charu C. Aggarwal ISBN-10 书号: 3319141414 ISBN-13 书号: 9783319141411 Edition 版本: 2015 出版日期: 2015-04-14 pages 页数: (734 ) $89.99 This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.

数据挖掘领域里程碑意义的经典著作！不可不看！原书第三版较翻译版，表述更精准。本资源为epub格式，可以转为mobi、pdf等格式。方便在手机、kindle、pc端阅读。

学习数据挖掘的经典教材，全面详实。

本人数据挖掘课程设计 利用pycharm +python进行开发 有详细的文档说明