test dtm 1 on the basis of dtm..so that 1 can predict the categories of dtm1

Question

library functions

     library(tm)
     library(e1071)
     library(plyr)

Inserting the journal names to be categorized

sample = c(
    "An Inductive Inference Machine",
    "Computing Machinery and Intelligence",
    "On the translation of languages from left to right",
    "First Draft of a Report on the EDVAC",
    "The Rendering Equation")
corpus <- Corpus(VectorSource(sample))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, stemDocument,language="english")
corpus <- tm_map(corpus, stripWhitespace)
dtm <- DocumentTermMatrix(corpus)

term document matrix as training set

inspect(dtm)
Category=c("Machine learning","Artificial intelligence","Compilers","Computer   architecture","Computer graphics")

declaration of the categories

my.data=data.frame(as.matrix(dtm),Category)
my.data 
sample = c(
    "gprof: A Call Graph Execution Profiler",
    "Architecture of the IBM System/360",
    "A Case for Redundant Arrays of Inexpensive Disks (RAID)",
    "Determining Optical Flow",
    "A relational model for large shared data banks",
    "some complementarity problems of z and lyoponov like transformations on       edclidean  jordan algebra")
corpus <- Corpus(VectorSource(sample))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, stemDocument,language="english")
corpus <- tm_map(corpus, stripWhitespace)
dtm1 <- DocumentTermMatrix(corpus)

term document matrix as testing set

inspect(dtm1) 

Answer 1

Well, your sample data has absolutely no overlapping terms, so there's not really much you can do there. The tm library doesn't assign meaning to words, it just measures their correlation. So you need to supply enough overlapping data so that is has a chance of matching up new input to an existing corpus.

Once you actually have real data, you have many options on how you want to build a model. You can use a kNN classifier like that in the class package, or a decision tree like that in the rpart package, or a neural network like that in the nnet package. There are examples of each of those in this presentation. But it's up to you to decide what's right for your data. That part is not a programming related question.