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The previous article introduced the open source data mining software Weka to do Association rules mining, Weka convenient and practical, but can not handle large data sets, because the memory is not fit, give it more time is usele
theory of Zhouyi is the similarity, relevance and holographic principle of everything. These three principles have been confirmed by modern science. Holographic means that a part of a thing contains the whole information. For example, a forensic worker tests a hair to obtain many of the physical characteristics of a victim or suspect. The book of changes predicts the future state of things by accumulating experience through the study of historical ev
similarity between objects . The more common methods used to measure the similarity of objects are distance , density and so on.The principle of cluster analysis can be viewed as follows:To group cards:According to the color of:Divide by symbol:By color:By the size of the degree of similarity:Here is an example of a cluster:3. ForecastThere are similarities between data mining prediction and Zhouyi predict
and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification.I've seen a part of this book before, but the internship involves working with the
strength is statistical analysis, which provides a wide range of parametric and parametric testing methods. At the same time, there are many feature selection methods. WEKA WEKA (Waikato environment for knowledge analysis, http://www.cs.waikato.ac.nz/ml/weka/) may be the most famous open source machine learning and data
be mining. Therefore, you only need to feed the data to the ml algorithm and it can make judgments for you, instead of worrying about the specific process. 1.3 computational approaches to Modeling I talked about two models before. How can I use the discovery model? There are using different approaches to modeling data. Here we will introduce two types, 1.Summar
I plan to organize the basic concepts and algorithms of data mining, including association rules Mining, classification, clustering of common algorithms, please look forward to. Today we are talking about the most basic knowledge of association rule mining. Association rules minin
selection condition is secondary, just how to build a good model. But in data mining, it's not exactly the case. In data mining, the guidelines play a central role. (There are, of course, some independent exceptions to the norm in statistics.) GIFI's nonlinear multivariate analysis of schools is one of them. For examp
,I2,I3} has a number of occurrences of 2,{i1,i2}, so the confidence level is 2/4=50%Similarly, it can be calculated{i1,i3}=>i2,confidence=50%{i2,i3}=>i1,confidence=50%i1=>{i2,i3},confidence=33% i2=>{i1,i3},confidence=28%i3=>{i1,i2},confidence=33%That is, when a user buys a i1,i3, the system can refer i2 together as a package to the user, as these three items are frequently purchased together.However, through the description of the entire process of the algorithm, we can see that the Apriori algo
(after pruning) whether the operator is handling the alarm in a timely manner2. Calculate the impact of the various dimensions on the final decision (the information gain rate) is branched from high to low.3.c4.5 is also the first of the top ten algorithms for data Mining (J48 in Weka)Classification-Supervised learningDecision Tree: CLS (most basic), ID3 (inform
1, RapidMiner The tool is written in the Java language and provides advanced analysis techniques through a template-based framework. The biggest benefit of this tool is that users don't have to write any code. It is provided as a service rather than as a local software. It is worth mentioning that the tool topped the list of data mining tools.In addition to data
This is a computer database storage and management class of high-quality pre-sale recommendation "MATLAB data Analysis and mining actual combat". A number of senior data mining experts more than 10 years of practical experience crystallization, in-depth interpretation of the various aspects of
only 1. So the count of conditional pattern bases is determined by the minimum count of nodes in the path.Depending on the conditional pattern base, we can get the conditional FP tree for that commodity, for example i5:According to the conditions of the FP tree, we can do a full array of combinations, to get the frequent patterns excavated (here to the commodity itself, such as i5 also counted in, each commodity mining out of the frequent pattern mus
Data Mining: Concepts and technologiesBasic InformationOriginal Title: Data Mining: concepts and techniques, Third EditionAuthor: (US) Jiawei Han University of Illinois-erbana-shangpain (plus) mirine kamber Simon-Fraser University (plus) Jian Pei Simon-Fraser University [Introduction to translators]Translator: Fan Ming
, factor analysis, missing value processing. In addition, you can read Liusi Zhe's "153 minutes to learn R." This book collects the 153 most frequently asked questions for beginners in R. Why call it 153 minutes? Because the original author wrote 153 questions, it took 1 minutes to read a question, and it was 153 minutes in the global.2. Advanced IntroductoryAfter reading the above books, you can go to the advanced entry stage. There are two very clas
Copyright belongs to the author.Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.Tan XinLinks: http://www.zhihu.com/question/21380122/answer/22156159Source: KnowBig Data has two directions, one is computer-biased and the other is economy-biased. You've learned Java, so you can shot computerBasis1. Reading "Introduction to Data
In today's big data era, data is money. With the transition to an application-based domain, data shows exponential growth. However, 80% of the data is unstructured, so it requires a program and method to extract useful information and convert it into an understandable and available structured form. A large number
Just a few, say something:Basic article:1. Reading "Introduction to Data Mining", this book is very easy to understand, there is no complex advanced formula, very suitable for people to get started. You can also use this book for reference "Data mining:concepts and Technique
Look at the algorithm theory of business intelligence software data mining often feel some formula derivation process such as Heavenly Book general, for example, look at the mathematical proof of SVM, EM algorithm:, the sense of knowledge jumps relatively big, then the data mining
. Pruning, satisfying the support and credibility of these 1-itemsets move to the next round of processes, and then look for the 2-itemsets that appear. Repeat, the itemsets for each level are repeated, knowing the size of the itemsets we defined earlier. is the algorithm supervised or unsupervised? Apriori is generally considered an unsupervised method of learning, as it is often used to excavate and discover interesting patterns and relationships.But, wait, there is ... The Aprior
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