Impact Analysis of Harassment Against Women Using Association Rule Mining Approaches: Bangladesh Prospective
This addresses the issue of harassment against women, especially students, in Bangladesh, but it is incremental as it applies existing data mining methods to a new dataset.
The paper tackled the problem of analyzing harassment impacts on women in Bangladesh using association rule mining, finding specific vulnerable groups and factors through survey data and comparing Apriori and F.P. Growth algorithms, with performance metrics briefly discussed.
In recent years, it has been noticed that women are making progress in every sector of society. Their involvement in every field, such as education, job market, social work, etc., is increasing at a remarkable rate. For the last several years, the government has been trying its level best for the advancement of women in every sector by doing several research work and activities and funding several organizations to motivate women. Although women's involvement in several fields is increasing, the big concern is they are facing several barriers in their advancement, and it is not surprising that sexual harassment is one of them. In Bangladesh, harassment against women, especially students, is a common phenomenon, and it is increasing. In this paper, a survey-based and Apriori algorithm are used to analyze the several impacts of harassment among several age groups. Also, several factors such as frequent impacts of harassment, most vulnerable groups, women mostly facing harassment, the alleged person behind harassment, etc., are analyzed through association rule mining of Apriori algorithm and F.P. Growth algorithm. And then, a comparison of performance between both algorithms has been shown briefly. For this analysis, data have been carefully collected from all ages.