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0 Prerequisites: a working Java Run Time Environment >= 1.6.0 We have successfully tested on Java version 1.6.0 and 1.7.0. Note: it's known not working for Java 1.5.0.
1 Download the cfc.zip file;
2 Unzip cfc.zip file to a folder;
3 Keep the folder structure: ## working folders cfc ## current working folder cfc\20_newsgroup ## 20-newsgroup corpus, obtained from http://kdd.ics.uci.edu/databases/20newsgroups cfc\out ## directory for output results cfc\conf ## config file's folder ## executable jar file 20-parser-sjtu.jar ## parsing 20-newsgroup corpus into a series of files cfc-sjtu.jar ## getting the testing result in this corpus ## help file readme.txt
4 Change to directory "cfc" in command line mode;
5 Run 20-parser-sjtu.jar file with this command: java -jar 20-parser-sjtu.jar
This command would help you to parse 20-newsgroup corpus and get some important statistic information.
You should get some running information such as:
"finish in getting category and file information ..."
"finish in coping No-1 class: alt.atheism"
If you can not get those running information, you'd better assure your java's running environment.
6 Run cfc-sjtu.jar file with this command: java -jar cfc-sjtu.jar
This command produce the testing results for the 20-newsgroup corpus.
7 Read the result from "out" folder
20-all_idf.txt ## total IDF(Inversed Document Frequency) statistic information
20-inner_df.txt ## total inner-class distribution information
## each line represents a category
20-inter_df.txt ## inter-class distribution information
## similar to document frequency among the categories
20-newsgroup.txt ## total term frequency information in 20-newsgroup
20-newsgroup_vector.txt ## total TF-IDF scores for every document
categorizeslist.txt ## category name
cfc_test_sim.txt ## 6635 texts' testing information using CFC's classification method.
## Each line represents the similarity
## scores between a document and 20 categories
## (normalized by the maximum value of each line).
## For the whole corpus (from 0 to the maximum), testing documents are those:
## (document index) mod 3 == 0
cfc_train_sim.txt ## training texts' result for CFC's classification
## similar to cfc_test_sim.txt
test_cfc_stat_result.txt ## testing result's statistic information, for each category,
## the format is:
## Number of Documents, True Positive, False Negative, False Positive
8 Introduction to CFC CFC is a centroid-based classifier for multi-class text categorization. It is built from two important class features: inter-class term distribution and inner-class term distribution.
Note:
In 20-inner_df.txt, a higher value would mean a good discriminative word.
In 20-inter_df.txt, a higher value would mean a worse discriminative word among categories.