both1112 2020-04-03 09:48:07
Description: The multi-classification data recognition under SVM was completed
zimomoz 2020-03-30 09:51:52
Description: the answer of machine learning textbook, practice the answers in R language.

Description: Clean the data and analyze the subject through LDA

Description: FPtree application and data mining, association, explore frequent item set, thus mining association rules.

Description: Detailed python implementation of FP-TREE for association rule mining, python3.2 implementation, can generate pictures of fp tree at each step
zdsjjt12 2020-02-29 22:17:44
Description: Big data mining tutorial full of dry goods, divided into two volumes (file size limit)

Description: Configuration of data mining
jiangling 2019-12-31 20:27:05
Description: LASSOfjlakjlakjslkajssasjdklajsdlkajslk
FunkyM 2019-11-11 00:16:04
Description: Simple example of naive Bayesian algorithm
Forrest-gan 2019-11-05 16:05:21
Description: A good data mining tutorial

Description: theu ffsdndf frrngngur fr
DecentMan 2019-07-23 19:18:52
Description: Matlab code for data mining and some samples for easy usage!!!
cychin 2019-07-18 21:11:36
Description: Cluster and mark the unlabeled bank data and analyze the data

Description: Through this book, readers can not only master the method of using R and relevant algorithm packages to quickly solve practical problems, but also start from the analysis of practical problems to the comprehensive training of using R to solve problems and analyzing mining results
O亚明O 2019-07-09 18:06:57
Description: This code is a simple code for big data operation, used for massive data mining.

Description: Clustering analysis is a common algorithm in our data mining. It is often used in sample research without classification but with related similarities. It includes three algorithms: K-Means, K-centers and system clustering. They have their own characteristics and applicable environment. Here is the classic K-Means clustering algorithm, and output graphics intuitive view.

Description: KPCA, the Chinese name "Kernel Principal Component Analysis", is a non-linear extension of PCA algorithm. In other words, PCA is linear, and it often seems powerless for non-linear data. For example, there must be a non-linear relationship between face images of different people. The experiments based on ORL data set made by ourselves can be achieved by PCA. Recognition rate is only 88%, but also unsupervised learning KPCA algorithm can easily achieve about 93% recognition rate (although the main purpose of both is to reduce dimension, not classification, but also can be used for classification), which is largely due to the fact that KPCA can mine the non-linear information contained in the data set.
Jimmyw 2019-07-02 17:14:15
Description: Data mining, as a very useful way at this stage, is applied in various fields. We can learn from it.
dfdf324324 2019-06-24 10:46:22
Description: Introduction to Data Mining Papers Electronic Edition, including Naive Bayesian and Priori Algorithms and Source Code
youyouyou12 2019-06-14 17:53:50
Description: The system architecture and the meta data in data warehouse are determined.
article 2019-06-13 15:15:18
Description: Data mining examples, kaggle, Titanic data mining
Description: Python implementation of apriori, id3, c4.5, FP Tree and other algorithms
VirtualWorld 2019-06-02 23:30:27
Description: Introduction and Practice of Python Data Mining Source Code

Description: Data mining learning material
johncolon 2019-05-14 22:47:46
Description: Some good papers on medical data mining