频繁关闭项目集的集合比所有频繁项目集的集合小得多。 同时，它保持完整性。 在本文中，我们提出了一种基于ITBitree的算法，称为ITBitreeFCIM（ITBitree频繁关闭项目集挖掘器），用于直接挖掘频繁关闭项目集。 定义了一种称为ITBitree（Itemset Tidset二叉树）的新颖结构来存储事务和项集信息。 通过使用自上而下的策略从该树中深入搜索每一层中的右节点以及它们各自的搜索空间中的右节点，可以直接生成频繁的封闭项集。 对于相邻层中具有相同支撑的项目或具有相同Tid的项目，我们使用项目合并技术来修剪搜索空间。 搜索的空间用于避免生成重复项和进行大量的关闭检查。 在此过程中，我们不需要检查候选项目集是否已关闭，因此节省了大量时间。 实验证明了该方法的有效性。

资源中包含书中代码、数据集和运行结果pdf文档

IS31FL3193_DS-1949526.pdf

GeekBrains_Data_Mining

In this paper, we propose a global and local tensor factorization (GLTF) to solve the multi-criteria recommendation problem. It leverages additional criterion-specific ratings in addition to existing user-item rating data for better recommendation. Moreover, it can jointly learn a global predictive model and multiple local predictive models, not only can discover the overall structure of the entire rating tensor, but also capture diverse rating behaviors of users in individual sub-tensors...The

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。