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频繁项集挖掘算法--CFPGROWTH算法
数据挖掘经典算法，频繁项集挖掘经典算法，CFPGROWTH算法，JAVA实现，代码中有详细注释
- 2023-03-29 10:25:03下载
- 积分：1
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Kmeans-python
聚类分析31省市的经济情况，以每个聚类簇的平均值来衡量省市经济的发展水平。(Cluster analysis of the economic situation of 31 provinces and municipalities, with the average value of each cluster to measure the level of economic development of provinces and municipalities.)
- 2020-07-03 13:40:02下载
- 积分：1
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聚类指标小结
说明： 聚类评价指标的各种说明，非常详细，请仔细阅读。(Cluster evaluation indicators of various descriptions, very detailed.)
- 2020-06-19 05:20:01下载
- 积分：1
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degreeor
复杂网络聚类系数的matlab编程代码,将复杂网络存储为矩阵,再对其matLab编程,可得到度分布图(The MATLAB programming code of clustering coefficient of complex network is used to store complex network as matrix, and then matLab programming is used to get the degree distribution graph.)
- 2018-05-23 05:28:45下载
- 积分：1
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eyboi737
基于P-Q分解法的快速潮流计算,可用于实际系统()
- 2017-12-05 16:20:12下载
- 积分：1
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Python数据预处理
Python数据预处理示例，包括数据清洗、数据整合、数据变换等操作。(Python data preprocessing examples, including data cleaning, data integration, data transformation and other operations.)
- 2020-09-17 14:07:54下载
- 积分：1
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分位数回归
说明： 多种方法实现分位数回归,有完整原理解释，直接可用。(Multiple methods for quantile regression)
- 2020-03-03 14:37:14下载
- 积分：1
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Python for Data Analysis
利用python进行数据分析，英文书籍，从pandas库的数据分析工具开始利用高性能工具对数据进行加载、清理、转换、合并以及重塑；利用matpIotlib创建散点图以及静态或交互式的可视化结果；利用pandas的groupby功能对数据集进行切片、切块和汇总操作；处理各种各样的时间序列数据。(Starting from the data analysis tools of pandas database, high performance tools are used to load, clean, transform, merge and remodel data; scatter plots and static or interactive visualization results are created by matpIotlib; data sets are sliced, sliced and aggregated by pandas group by function; and various operations are processed. Time series data.)
- 2020-06-16 03:20:01下载
- 积分：1
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holt_winters
holt-winters 三次指数平滑算法,时间序列预测算法，带测试数据及Demo(Triple Order Exponential Smoothing, Holt-Winters algorithm, time series prediction algorithm with test data and Demo)
- 2016-10-10 18:57:02下载
- 积分：1
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pu_ju_lei
将数据集转换为拉普拉斯矩阵，然后利用基于图论的谱聚类进行聚类。拉普拉斯矩阵采用高斯核函数，全连接方法计算。谱聚类擅长处理高维数据或非凸数据集。(The data set is transformed into Laplacian matrix, and then clustered by spectral clustering based on graph theory. The Laplacian matrix is calculated by using the Gauss kernel function and the full connection method. Spectral clustering is good at dealing with high-dimensional or non-convex data sets.)
- 2019-07-01 16:05:39下载
- 积分：1
