weka data mining tool

, simulated annealing algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learnin

{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

whether there is no thought of the data state Tao factors are related to the power of these content is the first to explore the characteristics of the door analysis exploration Data Tao visual The most ideal way to operate the portThird electrical technology selection and data adjustment, the problem of the clear door want to solve the problem more clear when th

those who want to get the basic and thought of r language and data mining, they want to use r language in practice to solve problems in school students and in-service workers.Syllabus:First Lecture: The Essentials of R languageIn order to gradually and cover the R language important and useful basic content principles, this talk from the beginning of the introduction of the R language, with the previous

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

to the enterprise. Some people say that data mining is only "disappointing", it looks marvellous, but nothing useful. This is a misunderstanding, admittedly, in some data mining projects, or because of a lack of clear business goals, or because of inadequate data quality, o

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

algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on learning sort):Pointwise:mcrank;Pairwise:ra

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on learning sort):Pointwise:mcrank;Pairwise:rankingsvm,r

algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on l

the form of star schema or snow flake schema. 12. OLAP (on-line analytical processing) Online Analysis System A) is part of DST (decision support tool) B) use traditional query and report forms to describe information in the current database C) OLAP is mainly used to show why a business model is correct, that is, to verify the correctness of a "knowledge" (opposite to data

Label:Oracle database file deleted, restore Linux deleted data file via Extundelete There's a friend's client in the group today. RM dropped the data file, and then discussed the use of extundelete to recover, salvage some of the data files are not overwritten. The official address of the software: http://extundelete.sourceforge.net/ 1. Installing the Extundelete

separately and recommend some resources that will help us better understand machine learning and improve related skills. This classification of the learning phase is only my personal advice, and perhaps there are some resources in the pre-and post-classification phases that are appropriate for the current phase. I think it is very helpful to have a holistic understanding of machine learning, and I would like to hear your thoughts and tell me through the comments below! Beginner Stage Beginners

words, whether it is the same sales strategy .... What kind of sales strategy is better suited for that type of product? Will the sales of the various products affect the sales? is not suitable for us to do bundle sales.We can solve these problems through the Microsoft Time Series algorithm, which is the application scenario of the algorithm, gossip, we enter the topic of this article.Technical preparation(1) Also we use the case Data Warehouse provi

Tags: style blog http io color ar os for SPOriginal: (original) Big Data era: a summary of knowledge points based on Microsoft Case Database Data Mining (Microsoft Time Series algorithm)ObjectiveThis article is also the continuation of the Microsoft Series Mining algorithm Summary, the first few mainly based on state d

analysis process I will summarize what each algorithm can do and what it can analyze.Here we go to the topic, through a simple process configuration we can implement the entire data mining process, followed by the following steps1. New project, configure Data sourceThis is nothing to analyze, based on the Microsoft case database to establish a

developed platforms. When a data mining tool runs on a high-performance parallel processing system, it can analyze a very large database in a few minutes. This faster processing means that users have more opportunities to analyze the data, make the results of the analysis more accurate and reliable, and easy to unders

. Are the sales laws in different regions consistent? In other words, whether it is the same sales strategy .... What kind of sales strategy is better suited for that type of product? Will the sales of the various products affect the sales? is not suitable for us to do bundle sales.We can solve these problems through the Microsoft Time Series algorithm, which is the application scenario of the algorithm, gossip, we enter the topic of this article.Technical preparation(1) Also we use the case

opportunities. The above characteristics of CRM are not isolated from each other, but a whole of mutual support and high integration, which compose the powerful function of CRM. 3, the implementation of CRM and data mining technology 3.1, the composition of the CRM solution CRM as Enterprise management system software, usually consists of the following three parts: "Networked Marketing management System (s