data mining with rattle and r

A data warehouse can be used as a data source for data mining, OLAP, and other analysis tools. Because the data stored in a data warehouse must be filtered and converted, the wrong data

Reprint: http://www.cnblogs.com/zhijianliutang/p/4050931.htmlObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data

Ck:candidate itemset of size klk:frequent itemset of size kL1 = {Frequent items};for (k = 1; Lk! =?; k++) does begin Ck+1 = candidates generated from Lk; For each transaction t in database does increment the count of all candidates in ck+1 that is contained in T lk+1 = candidates in ck+1 with Min_support Endreturn? k Lk;SQL applicationSuppose the items in Lk-1 is listed in a orderstep 1:self-joining Lk-1 insert INTO Ckselect p.item1, p.item2, ..., P.item K-1, Q.itemk-1from Lk-1 p,

predictable, the algorithm generates a separate decision tree for each predictable column.The principle of the algorithm:The Microsoft decision tree algorithm generates a data mining model by creating a series of splits in the tree. These splits are represented as "nodes". Whenever an input column is found to be closely related to a predictable column, the algorithm adds a node to the model. The algorithm

Http://www.cnblogs.com/captain_ccc/articles/4093652.html This article is also the continuation of the Microsoft Series Mining algorithm Summary, the previous several mainly based on state discrete value or continuous value for speculation and prediction, the main algorithm used is three: Microsoft Decision tree Analysis algorithm, Microsoft Clustering Analysis algorithm, Microsoft Naive Bayes algorithm , of course, the follow-up also added a result

Defined Data Mining is the nontrivial process of acquiring effective, novel, potentially useful, and ultimately understandable patterns from large amounts of data stored in databases, data warehouses, or other repositories. What is the use of. Data

How can we fully understand "Data Mining "? What is the theoretical basis of "data mining? Figure 1 shows:In reality, human social and economic activities can always be described and recorded using data (numbers or symbols). After analyzing these

Validating a data mining model Typically, for a particular case, we can't pinpoint which mining algorithm is the most accurate, so we define multiple mining models in a mining structure, and we get the most accurate one by validating multiple

The algorithm in this paper only outlines the core idea, the specific implementation details of this blog "Data Mining Algorithm learning" classification under other articles, not regularly updated. Reprint please indicate the source, thank you.Referring to a lot of information and personal understanding, the ten algorithms are categorized as follows:? Classification algorithm: C4.5,cart,adaboost,naivebayes

Data | Database with the development of database technology and the extensive application of database management system, the amount of data stored in the database has increased dramatically, and many important information is hidden behind a large amount of data, if the information can be extracted from the database, it will create many potential profits for the c

Label: style blog HTTP Io use AR for strong File In the previous article, we roughly introduced some knowledge about data mining. Let's talk about the data problems in data mining. There is no doubt that in data