weka data mining

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

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

Data warehouse and data mining are two big concepts. They are very mature in foreign countries. In China, with the accumulation of enterprise data and the maturity of ERP in the past few years, data warehouse and data

Reprinted from Http://reader.dashuai.net/?p=100Data Cleansing Class toolDatawranglerGoogle RefineStatistical analysis class ToolsThe R Project for statistical ComputingTimeflowData Presentation class ToolsGoogle Fusion TablesImpureTableau PublicMany EyesVIDIZoho ReportsCode Helper Class ToolChooselExhibitMap-related data display toolsQuantum GIS (QGIS)OpenheatmapOpenlayersText class related processing toolsIBM Word-cloud GeneratorSocial Network class

Http://www.cognoschina.net/club/thread-66425-1-1.html for reference only "Automatic Big Data Mining" is the true significance of big data. Nowadays, big data cannot work very well. Almost everyone is talking about big data. But what is big

Tags: blog http os using ar strong file Data spThis article is mainly to continue on the two Microsoft Decision Tree Analysis algorithm and Microsoft Clustering algorithm, the use of a more simple analysis algorithm for the target customer group mining, the same use of Microsoft case data for a brief summary. Interested students can first refer to the above two a

Reprint: http://www.cnblogs.com/zhijianliutang/p/4067795.htmlObjectiveFor some time without our Microsoft Data Mining algorithm series, recently a little busy, in view of the last article of the Neural Network analysis algorithm theory, this article will be a real, of course, before we summed up the other Microsoft a series of algorithms, in order to facilitate everyone to read, I have specially compiled a

A lot of good papers were quoted in this paper, so I read this 06 paper. Abstract Introduces 10 challenging questions in data mining and a high-level guide to analyzing where data mining problems are occurring. This article was written by the author by consulting some of the most active

Reprint: http://www.cnblogs.com/zhijianliutang/p/4021799.htmlObjectiveThis article is also the continuation of the Microsoft Series Mining algorithm Summary, the first few mainly based on state discrete values or continuous values for speculation and prediction, the algorithm used mainly three kinds: Microsoft Decision tree Analysis algorithm, Microsoft Clustering algorithm, Microsoft Naive Bayes algorithm , of course, followed by a summary of the res

The 1th Chapter Introduction　　Data mining is a technology that combines traditional methods of data analysis with complex algorithms for processing large amounts of data. Data Mining provides an exciting opportunity to explore and

If you have a shopping website, how do you recommend products to your customers? This function is available on many e-commerce websites. You can easily build similar functions through the data mining feature of SQL Server Analysis Services. It is divided into three parts to demonstrate how to implement this function. 1. Build a Mining Model 2. Compile service in

When it comes to data mining, we tend to focus on algorithms during modeling while ignoring other steps. In real world data mining projects, other steps are the key to determining project success or failure. Guide to intelligent data analysis is the book recommended by the k

With the rapid development of database technology and the widespread use of electricity in the database of electric storage data is more and more large gate in the field of data mining to use scientific methods, method to reduce the time of mining algorithm to make data

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

1. Data analysis and data mining linkages and differencesContact: are engaged in data differences: data analysis of the statistical, visualization, reporting and reporting, the need for strong expression ability. The data

Depending on the data mining that you've heard or seen countless times, do you know what that is? Many scholars and experts give different definitions of what data mining is, and here are a few common statements:"To put it simply, data m