readme

README.md

@@ -1,14 +1,43 @@
 ## Introduction
 This repository is used to store all the code and data we use to produce experimental results in paper "Towards Effective Log Clustering"
 
-Data folder contains all the data we use for experiments.
+## Organization of the repository
+- data folder: Contains all data files that are used in the experiments
+- figure folder: This folder is used to store all the output figures from experiments
+- evaluation.R: contains implementation of 3 clustering validation measures including (average silhouette coefficients, Dunn Index, BetaCV). It also contains function to provide plot for distribution of silhouette coefficients.
+- utils.R: other supporting functions such as reading distance matrix.
+- script_figure_2.R: produce Figure 2 as shown in the paper.
+- script_figure_3.R: produce Figure 3 as shown in the paper.
+- script_figure_4.R: produce Figure 4 as shown in the paper.
 
 ## Reproducing experimental results
 ### Reproduce figure 2
 In order to reproduce distribution of silhouette coefficients when using Aligon similarity without regularization and when regularization is applied as shown in Figure 2 of the paper, users can open the file *script_figure_2.R*. Running this script file will produce the silhouette plots in folder *figure*.
 
 ### Reproduce figure 3
-In order to reproduce the plots for comparison between three similarity metrics (Aligon, Aouiche, Makiyama) on three datasets (IIT Bombay, UB Exam and PocketData-Google+ datasets) with and without regularization as shown in Figure 3 of the paper, users can use the file *script_figure_3.R*. Running this script file will produce the corresponding figures in folder *figure*.
+In order to reproduce the plots for comparison between three similarity metrics (Aligon, Aouiche, Makiyama) on three datasets (IIT Bombay, UB Exam and PocketData-Google+ datasets) with and without regularization as shown in Figure 3 of the paper, users can use the file *script_figure_3.R*. This script requires an input file *result.csv* in *data* folder. The number in *result.csv* can be filled in by running the following commands in R:
+
+    # load two files evaluation.R and utils.R
+    source(file = "./evaluation.R")
+    source(file = "./utils.R")
+
+    # load supporting libraries
+    library(cluster)
+    library(factoextra)
+    library(RColorBrewer)
+
+    # read data file
+    dataset <- read.csv(file = "./data/bombay_queries.csv", header = TRUE, sep = "\t")
+
+    # read distance matrix
+    distMat <- readDistMat("./data/bombay_aligon.csv") 
+
+    # print different clustering validation measures
+    print(avgSilhoette(distMat, dataset$label))
+    print(BetaCV(distMat, dataset$label))
+    print(DunnIndex(distMat, dataset$label))
+
+When the input file is ready, running 'script_figure_3.R' file will produce the corresponding figures in folder *figure*.
 
 ### Reproduce figure 4
-In order to reproduce the plots for comparing the effect of different modules in regularization as shown in Figure 4 of the paper, users can use the file *script_figure_4.R*. Running this script file will produce the corresponding figures in folder *figure*.
+In order to reproduce the plots for comparing the effect of different modules in regularization as shown in Figure 4 of the paper, users can use the file *script_figure_4.R*. This script requires an input file *modules.csv* in *data* folder. Running this script will produce the corresponding figure in folder *figure*.