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worth mentioning that the tool is ranked top of the data Mining tool list.In addition to data mining, RapidMiner also provides features such as data preprocessing and visualization, predictive analysis and statistical modeling, evaluation, and deployment. What's more, it al
The purpose of data mining is to find more high-quality users from data. Next, I went on to discuss the data mining method model in the previous blog. What is a guided data mining metho
better implementation, go to WEKA source code or www. helsinki. fi/...s.html ~But in fact, it is annoying to understand what people have written, and the idea of "Apriori" is very basic. Java also has a lot of useful collection classes. I can write usable classes in just one day ~Apriori algorithm Data Mining I think weka
necessary to provide a well-categorized training data set, so the cart is a supervised learning algorithm. Why use a cart?Most of the reasons for using C4.5 also apply to cart, as they are all methods of decision tree learning. The reasons for this type of explanation are also applicable to the cart. As with C4.5, they are computationally fast, the algorithms are generally popular, and the output is readable. Scikit-learn implements the CART algori
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
/~ Mlearn // mlrepository.htm Statlib Http://liama.ia.ac.cn/SCILAB/scilabindexgb.htm Http://lib.stat.cmu.edu/ Sample Database Http://kdd.ics.uci.edu/ Http://www.ics.uci.edu /~ Mlearn/mlrepository.html Websites for fund Data Mining Http://www.gotofund.com/index.asp Http://lans.ece.utexas.edu /~ Strehl/ Reuters Dataset Http://www.research.att.com /~ Lewis/reuters21578.html Various datasets: Http://kdd.ics.uc
Data Mining is the non-trivial process of obtaining effective, novel, potentially useful, and ultimately understandable patterns from a large amount of data. A broad view of data mining: Data
{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,labe
This code can be downloaded in http://download.csdn.net/detail/fansy1990/8502323.In the previous article, the Hotspot Association rule Algorithm (1)-mining discrete data analyzes the hotspot Association rules of discrete data, and this paper analyzes the mining of the Hotspot Association rules of discrete and continuou
search and the intersection of sets: Eclat 4. Sequence mode Commonly used packages: Arulessequences Spade algorithm: Cspade 5. Time series Commonly used packages: Timsac Time series build function: TS Component decomposition: Decomp, decompose, STL, TSR 6. Statistics Commonly used packages: Base R, Nlme Variance analysis: AoV, ANOVA Density Analysis: Density Hypothesis test: T.test, Prop.test, Anova, AoV Linear hybrid Model:
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
pl1936-Big Data Fast Data mining platform RapidMiner data analysisEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tuto
analytical processing): Online Analytical Processing OLAP was proposed by E. F. codd in 1993.Definition by the OLAP Council: OLAP is a software technology that enables analysts to quickly, consistently, and interactively observe information from various aspects to gain an in-depth understanding of data, this information is directly converted from raw data. They reflect the real situation of the enterprise
Analytical Processing): Online Analytical ProcessingOLAP was proposed by E. F. Codd in 1993.Definition by the OLAP Council: OLAP is a software technology that enables analysts to quickly, consistently, and interactively observe information from various aspects to gain an in-depth understanding of data, this information is directly converted from raw data. They reflect the real situation of the enterprise i
When big data talks about this, there are a lot of nonsense and useful words. This is far from the implementation of this step. In our previous blog or previous blog, we talked about our position to transfer data from traditional data mining to the Data Platform for processi
analytical processing): Online Analytical Processing OLAP was proposed by E. F. codd in 1993.Definition by the OLAP Council: OLAP is a software technology that enables analysts to quickly, consistently, and interactively observe information from various aspects to gain an in-depth understanding of data, this information is directly converted from raw data. They reflect the real situation of the enterprise
or subject data (Subjectarea). In the process of data Warehouse implementation, it is often possible to start with a Department data mart and then make a complete data warehouse with several data marts. It is important to note that when implementing a different
Original: (original) Big Data era: a summary of knowledge points based on Microsoft Case Database Data Mining (Microsoft Decision Tree Analysis algorithm)With the advent of the big data age, the importance of data mining becomes a
This code can be downloaded (updated tomorrow).In the previous article, the Hotspot Association rule Algorithm (1)-mining discrete data analyzes the hotspot Association rules of discrete data, and this paper analyzes the mining of the Hotspot Association rules of discrete and continuous
In principle, data mining can be applied to knowledge mining in any information storage mode. However, the challenges and technologies of data mining vary with the Storage types of source data. In particular, recent studies show t
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