Home

Introduction

This repository is used to store all the code and data we use to produce experimental results in paper "Towards Effective Log Clustering"

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.

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. 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. This script requires an input file modules.csv in data folder. Running this script will produce the corresponding figure in folder figure.