1. The data analysis (Douban) book is quite simple. The basic content is involved, and it is clear. Finally, we talked about R as a plus.Difficulty level: very easy.2. Beer and diapers (Douban) are the most typical cases.Difficulty level: very easy.3. The beauty of data (Douban) An introductory book, each chapter solves a specific problem, and even has code, which is very helpful for understanding the appli
Entry books:
In-Depth Data Analysis (Douban)This book is quite simple. The basic content is involved, and it is quite clear. Finally, we talked about R as a plus. Difficulty level: very easy.
Beer and diapers (Douban)In this case, things are the most typical examples. Difficulty level: very easy.
Data beauty (Douban)Each chapter of an introductory book solves a specific problem and even contains code,
Original Author: Chandan Goopta. [Chandan Goopta is a data research expert from the University of Kathmandu (Nepal Capital) dedicated to building intelligent algorithms for affective analysis. ]
original link:http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/
In this day and age, it is no exaggeration to say that
(written in front) said yesterday to write a machine learning book, then write one today. This book is mainly used for beginners, very basic, suitable for sophomore, junior to see the children, of course, if you are a senior or a senior senior not seen machine learning is also applicable. Whether it's studying intelligence or doing other things, machine learning is a must. You see GFW all use machine study, we also have to science.(full-text structure) In fact, I think, learn a subject, List a p
Preface 1The first part of social network guidancePrologue 13The 1th Chapter explores Twitter: Exploring hot topics, discovering what people are talking about, etc. 151.1 Overview 15Reasons for 1.2 Twitter rage 161.3 Explore Twitter API 181.4 Analysis of 140 word tweets 331.5 Summary of this chapter 471.6 Recommended Exercises 481.7 Resources Online 482nd Chapter Mining Facebook: Analyzing fan pages, viewing friends, etc. 502.1 Overview 512.2 Explore
Data Mining-association analysis frequent Pattern Mining Java and C + + implementations of Apriori, Fp-growth, and Eclat algorithms:Website: http://blog.csdn.net/yangliuy/article/details/7494983Data Mining-Java implementation of newsgroup18828 text classifier based on Bayesian algorithm and KNN algorithm (top)http://bl
as the Greenplum database and HAWQ. The maintenance activities performed are open to the Apache community and ongoing academic research. If you only summarize the features of Madlib in one sentence, as described in the title, you can use SQL to play data analysis, data mining, and machine learning. 2. Features (1) Classification If the desired out
Today I saw in this article how to choose the model, feel very good, write here alone.More machine learning combat can read this article: http://www.cnblogs.com/charlesblc/p/6159187.htmlIn addition to the difference between machine learning and data mining,Refer to this article: https://www.zhihu.com/question/30557267Data mining: Also known as
JlqingData Mining-association analysis frequent Pattern Mining Java and C + + implementations of Apriori, Fp-growth, and Eclat algorithms:Website: http://blog.csdn.net/yangliuy/article/details/7494983Data Mining-Java implementation of newsgroup18828 text classifier based on Bayesian algorithm and KNN algorithm (top)http://blog.csdn.net/yangliuy/article/details/74
: Published in 2012, corresponding to Mahout version 0.5, is currently mahout the latest book books. At present, only English version, but a bit, the inside vocabulary is basically a computer-based vocabulary, and map and source code, is suitable for reading.? IBM mahout Introduction: http://www.ibm.com/developerworks/cn/java/j-mahout/Note: Chinese version, update is time for 09, but inside for Mahout elaborated more comprehensive, recommended reading, especially the final book list, suitable fo
The international authoritative academic organization theieeeinternationalconferenceondatamining (ICDM) selected ten classical algorithms in the field of data mining in December 2006: C4.5,k-means,svm,apriori,em , Pagerank,adaboost,knn,naivebayes,andcart.Not only the top ten algorithms selected, in fact, participate in the selection of the 18 algorithms, in fact, casually come up with a kind of can be calle
development of Baidu, Google. But with the rise of big data in recent years, crawler applications have been elevated to unprecedented heights. In terms of big data, in fact, their own data or user-generated data platform is very limited, only like e-commerce, micro-bo such a platform to avoid strong self-sufficiency,
, 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 classic books to read at this time. "Statistics w
Original address: http://blog.csdn.net/taigw/article/details/19407297In the 2006 ICDM (the IEEE international Conference on Data Mining), the top ten algorithms for data mining were selected, namely1,c4.5C4.5 is a series of algorithms used in machine learning and data
it together to see if this direction is feasible. I mainly want to know whether the full-text search, data mining, and recommendation engine technologies in your project can be applied to the health field ."Although this was Wu Yan's first attempt in the health field and the first time he thought about the application of full-text search, data
. Although these methods may provide some benefits, they will become impractical for the following two reasons: first, they require developers to spend time learning a query language that cannot be used in other cases. Second, they are not robust enough to handle inevitable simple changes to the target Web page.
In this article, we will discuss a web-based data mining method developed using standard web te
October 2006:848==================================Association analysis==================================#7. AprioriRakesh Agrawal and Ramakrishnan srikant. Fast Algorithms for MiningAssociation Rules. In Proc. Of the 20th Int ' L Conference on Very LargeDatabases (VLDB ' 94), Santiago, Chile, September 1994.Http://citeseer.comp.nus.edu.sg/agrawal94fast.htmlGoogle scholar Count in October 2006:3,639#8. Fp-treeHan, J., Pei, J., and Yin, Y. 2000. Mining
International authoritative Academic organization the IEEE International Conference on Data Mining (ICDM) 2006 12 The top ten classic data mining algorithms of the Month: C4.5, K-means, SVM, Apriori, EM, Pa Gerank, AdaBoost, KNN, Naive Bayes, and CART.No, but the top ten algorithms are selected. In fact , the selectio
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