similar to: Problem with Apriori

hello, I want to perform the Apriori association rules algorithm to my data. The set of data contains missing values and consists in a combination of continuous and categorical variables. After discretizing the continuous variables, I wrote the following instruiction to perform Apriori, but I obtained the following error message: > rules <- apriori(dd_new,parameter=list(supp=0.5, conf=0.9,

Hi All, I use arules library, and try to create an association rules for this transaction file: a,c,f,3,4,5 b,e,1,2,4 a,c,e,f,1,3,4,5 d,5 b,c,e,f,1,2,3,4 a,c,e,f,1,3,4,5 b,c,e,f,1,3,4 b,e,1,2,4 a,c,e,f,1,3,4,5 a,b,c,e,f,1,3,4 a,c,d,f,3,4,5 I want to get the rule such: {c,e,f}=> {3,4,5} I used this command: ar=apriori(tr, parameter=list(support=.4, confidence=0.8, maxlen=11), appearance

I am new to R( but quickly being awed by the range of this it can accomplish, you have one more convert to the useR universe). I have successfully implemented the apriori function and are getting great results. My question concerns how to export these results. I have read lots about write.csv functions and exporting data frames and other standard objects. Im having difficulty working with these S4

Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

Hi everybody, I am using the apriori() fn in the arules package and am encountered an error. rules <- apriori(rdayst,parameter = list(support = 0.01, confidence = 0.6)) "You chose a very low absolute support count of 0. You might run of memory." I assume this is related to the value of .01 specified for the support = argument. If so, what is a safe and reliable max value for

Dear list members, I know that there is the arules package with the implementation of the apriori algorithm. However i want to use the "predictive apriori" instead. These algorithm can mine as rules as i want and there is an implementation on weka. There is some implementation on R? -- Att, Flávio Barros [[alternative HTML version deleted]]

Hi, I am using Rattle 2.6.4 with R 2.12.2 on win64, is this a bug ? Following is the content after execute the associate analysis process: Summary of the Apriori Association Rules: Number of Rules: 23351 Summary of the Measures of Interestingness: support confidence lift Min. :0.1250 Min. :1 Min. :2.667 1st Qu.:0.1250 1st Qu.:1 1st Qu.:2.667 Median :0.1250

Hi, I recently got a bizarre message when running arules. It just said "Killed" and quit. Anyone know why this might have happened? I am running R on an AWS quad xl ubuntu instance. Here is some information, including dataset size and the parameters: parameter specification: confidence minval smax arem aval originalSupport support minlen maxlen 0.0003581251 0.1 1 none

https://bugs.freedesktop.org/show_bug.cgi?id=83897 Priority: medium Bug ID: 83897 Assignee: nouveau at lists.freedesktop.org Summary: GK106 (GeForce GTX 660): Frequent GPU lockups QA Contact: xorg-team at lists.x.org Severity: major Classification: Unclassified OS: Linux (All) Reporter: freedesk.apriori at

Dear R-users! I would like to search for "association rules" (and compute support and confidence, for example) in a data set, and wonder if this can be done with R (version 1.5.0 for Windows)? Particulary, I wonder if a) anyone has done something like that with R or b)maybe someone has written an R-interface to some existing algorithm/software (e.g. to something like the

Buenas tardes, Necesito ayuda con la función inspect() del paquete "arules". Mis comandos son: r1 <- apriori(tran, parameter=list(support=0.012, confidence=0.7)) r1 summary(r1) #todo bien, tengo 5 reglas y todo claro en el resume #pero al pedir inspect(r1) inspect(sort(r1, by = "confidence")) me arroja el error: Error en UseMethod("inspect", x) : no

I tried to create a domain with drbd based disk as the backend "drbd:<resourcename>,..," and I get a "unknown disk type" error. I can spin out a patch if needed, but just wanted to know apriori if this was left out for any specific reason. shriram _______________________________________________ Xen-devel mailing list Xen-devel@lists.xensource.com

Hi again everyone I have an arbitrarily dimensional array "a" and a list "jj" of length length(dim(a)). The elements of jj are vectors of indexes. How do I use do.call() to extract a[ jj[[1]], jj[[2]], jj[[3]], ...] ? Toy example follows: a <- matrix(1:30,5,6) jj <- list(5:1,6:1) I want the following a[ jj[[1]],jj[[2]] ] How do I do this? OBAttempts:

https://bugs.freedesktop.org/show_bug.cgi?id=79820 Priority: medium Bug ID: 79820 Assignee: nouveau at lists.freedesktop.org Summary: Strange readout on fan1_input when pwm1_min < 33 QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified OS: Linux (All) Reporter:

Hi All, I am trying to perform CCA and RDA several times to compare their performances. To do that I put this two functions of vegan package inside a for loop. Unfortunatly when I am trying to perform the anova(by=axis) to test the individual axes the function don't recognize the species data.frame. The same problem occurs whem I try to run forward selection. The same problem occur when I try

 Dear R experts   I generated rules using apriori method in arules package. Though I can access rules using %in% function but I am unable to access a specific rule by its unique identifier number. I want to use rule number for further analysis..   Thanking you in advance.   Daniel Amsterdam                                       Send instant messages to your online friends

Dear R help, I am analyzing sequences of events described by time and a unique event tag. And I am searching for recurring patterns where patterns have to show up in a certain time window, e.g. 5 or 10 minutes. Of course, inbetween these events other events may occur. I have applied basket analysis approaches like apriori or 'frequent item set' algorithms with interesting results but

Hi, I have been able to generate association rules for Market Basket Analysis using the following codes: **************************************************************************** ******************************************* library("arules") rules <- read.csv("write1.csv",na.strings=c(".", "NA", "", "?"),header=TRUE)

Hi All, I am trying to program the 'Tremor' decoder onto an array of Processors. I am using four Processors. I did this experiment: I split the while(!eof){} in the main() in to four tasks using if(!eof) statements. In this modification, each if() decodes one packet of data at-a-time, sequentially. Then, I ran the code on a single Processor and the decoding was successful. The

Question 2a) I am also working with arules package and I have the following problem let suppose the matrix b like: b<-matrix(c(1,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,1),nrow=6) rownames(b)=c("T1", "T2", "T3", "T4", "T5", "T6") colnames(b)=c("It1", "It2", "It3", "It4") bt<-as(b,