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统计数据的预处理源程序大全，数据的平滑处理，数据的标准化转换，数据的极差归一化变换(Daquan source of statistical data pre-processing, data smoothing, standardization of data conversion, data conversion of poor normalization)

用java实现的一种聚类方法, 应用了iris这个非常出名的数据 和glass这个非常出名的数据,很不错的,值得大家看看,()

说明：  深入理解Spark核心技术书籍，书籍对spark进行了深入讲解，并对spark源码进行了剖析(In-depth understanding of Spark core technology books, books on Spark in-depth explanation, and analysis of spark source code)

算术编码，熵编码，HUFFMAN编码的MATLAB实现。(Arithmetic coding, entropy coding, HUFFMAN encoded MATLAB to achieve.)

胡清华邻域粗糙集源码，matlab源代码，方便可用(Hu Qinghua neighborhood rough sets source code, matlab source code, easy to use)

最优化方法之近端梯度下降法，适用于L1正则项的最优化。(Proximal gradient descent)

线性拟合和二次拟合函数 pdg正交多项式作最小二乘拟合(Least Square fitting of Linear fitting and Quadratic fitting function pdg orthogonal polynomial)

Python是一种面向对象、解释型计算机程序设计语言，其应用领域非常广泛，包括数据分析、自然语言处理、机器学习、科学计算以及推荐系统构建等。 本书用Python语言来讲解算法的分析和设计。本书主要关注经典的算法，但同时会为读者理解基本算法问题和解决问题打下很好的基础。(Python is an object-oriented, interpretive computer programming language. It has a wide range of applications, including data analysis, natural language processing, machine learning, scientific computing and recommendation system construction. This book uses Python language to explain the analysis and design of algorithms. This book focuses on classical algorithms, but at the same time it will lay a good foundation for readers to understand basic algorithms and solve problems. The book consists of 11 chapters. The tree, graph, counting problem, inductive recursion, traversal, decomposition and merging, greedy algorithm, complex dependency, Dijkstra algorithm, matching and cutting problem, difficult problem and its dilution are introduced. The book has exercises and reference materials at the end of each chapter, which provides readers with more convenience for self-examination and further stu)

实现了克里金插值算法,在地质钻孔的加密和其他工程应用方面有重要的作用()

bigdata hive use for hadoop