bitcoin mining algorithm

Preface:For an introduction to the FP-GROWTH algorithm, see: Introduction to the FP-GROWTH algorithm.This paper mainly introduces the algorithm of extracting frequent itemsets from Fp-tree. See the above article for pseudo-code .The structure of the fp-tree is shown in the structure of the fp-growth algorithm Fp-tree (python).Text: TRee_mINeR.Py File

The address of this article is http://www.cnblogs.com/kemaswill/. the contact person is kemaswill@163.com. About DTWAlgorithmFor more information, see my previous blog: Time series mining-principles and implementation of the Dynamic Time normalization algorithm. DTW uses dynamic planning to calculate the similarity between two time series. The algorithm compl

PrefaceRecently on the data mining learning process, learn to naive Bayesian operation Roc Curve. It is also the experimental subject of this section, the calculation principle of ROC curve and if statistic TP, FP, TN, FN, TPR, FPR, ROC area and so on. The ROC area is often used to assess the accuracy of the model, generally think the closer to 0.5, the lower the accuracy of the model, the best state is close to 1, the correct model area is 1. The fol

From the beginning, I will introduce the classification problem, mainly introduce decision tree algorithm, naive Bayesian, support vector machine, BP neural network, lazy learning algorithm, stochastic forest and adaptive Enhancement Algorithm, classification model selection and result evaluation. A total of 7, welcome attention and Exchange. This article first

{String file = "D:/jars/weka-src/data/contact-lenses.txt"; int labelstateindex = 0; The target attribute is located under the subscript int maxbranches=2; Maximum number of branches double minsupport = 0.13; Minimum support double minconfidence=0.01;//minimum confidence (used in Weka is minimprovement) hotspot hs = new hotspot (); Hsnode root = Hs.run (file,labelstateindex,maxbranches,minsupport,minconfidence); System.out.println ("\ nthe rule tree as follows: \ n"); Hs.printhsnode (root,0);}}T

This series of articles mainly about the 2006 data Mining 10 large algorithms (see Figure 1). The focus of this article will be on the source of the algorithm and the main idea of the algorithm, and does not involve the concrete implementation. If there is any mistake in the text, I hope you will point it out and discuss it together.Figure 1 the article from Idme

--apriori Algorithm for association rules the concept of associative patterns in some of the discussions emphasizes the simultaneous occurrence of relationships while ignoring the sequence information in the data (time / space):time series : Customers buy product X, it is possible to buy products in a period of time Y;Spatial Sequence : A is found at a point , and it is possible to find the phenomenon Yat the next point. Example:customers who purchase

RELATED LINKSHttp://blog.csdn.net/column/details/datamining.htmlA popular understanding of the LDA topic modelhttp://blog.csdn.net/v_july_v/article/details/41209515Talking about Bayesian networks from the Bayesian approachhttp://blog.csdn.net/v_july_v/article/details/40984699Talking about spectral clustering from Laplace matrixhttp://blog.csdn.net/v_july_v/article/details/40738211The principle and derivation of Adaboost algorithmhttp://blog.csdn.net/v_july_v/article/details/40718799Mathematical

applications. Iv. Apriori algorithm [Basic Concepts] 1 [database]: stores the record set (D) of the two-dimensional structure; 2 [all item sets (items)]: the set of all items (I ); 3 [transaction]: A record (t, t belongs to D) in the database; 4 [itemset]: a set of items that appear at the same time. Defined as: K-itemset (k-itemset), K-itemset? T. Unless otherwise specified, the K values listed below represent the number of items. 5 [candidate item

Community Discovery algorithm for large-scale networks mining louvain--social networks = = = Algorithm source The algorithm derives from the article fast unfolding of communities in large networks, referred to as Louvian. algorithm principle Louvain

Frequent patterns are patterns that occur frequently in datasets, such as itemsets, sub-sequences, or sub-structures. For example, a collection of goods (such as milk and bread) that is frequently present in the transaction data set is a frequent itemsets.Some basic conceptsSupport: Supports (A=>B) =p (A and B)Confidence level: Confidence (a=>b) =p (b| AFrequent k itemsets: If the support degree of itemsets I satisfies the predefined minimum support threshold, I is called frequent itemsets, and

High-efficiency frequent mode mining algorithm PrePost and fin c ++ source code, prepostfin For the C ++ source code of PrePost, see http://www.cis.pku.edu.cn/faculty/system/dengzhihong/source%20code/prepost.cpp.For details about the Algorithm, see A New Algorithm for Fast Mining