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recsys-challenge2015
- 本代码实现了 recsys challenge2015数据集分析的算法，对数据挖掘和推荐系统的学习很有帮助~！-This code implements recommend algorithm based on recsys challenge2015 data set , which definitly would helpful for studying Data mining and Recommendation system !just enjoy
roughset-into-weka
- 可以嵌入weka中的粗糙集约简算法，进一步扩充weka的数据挖掘功能-Weka can be embedded in the rough set reduction algorithm to further expand weka data mining functions
Apriori
- 数据挖掘中的经典算法apriori。输入项集和最小支持度，输出频繁项集。-Data Mining the classical algorithm apriori. Entry and set minimum support, output frequent item sets.
data--preprocessing-using-kdd-data-set
- Data Mining process model selected is KDD which starts selection of data.Initially the researcher has taken the Kddcup.data-10-perecnt which contains total of 311,027 records which includes both labeled and unlabeled records-Data Mining process model
E-Algorithm
- 用于数据挖掘分类的算法，E-Alothm，并附有10个左右的KEEL专用数据集，算法实现+实际例程。-For the data mining classification algorithm, E-Alothm, and with 10 or so KEEL dedicated data set, the algorithm to achieve+ practical routines.
sklearn-tree-BN-knn
- 分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the dat