:: Volume 27, Issue 2 (3-2017) ::
مجله‌ی بررسی‌ها 2017, 27(2): 121-143 Back to browse issues page
Mining in Labor Force Survey Data Set to Extract Association Rules for Economical Inactive Population
Somaye Ahangar , Majid Khalilian * , Nariman Yousefi
Islamic Azad University, Karaj Branch
Abstract:   (3396 Views)
Nowadays, because of new methods in data collection technology, we have huge amount of data in organization. Due to important role of analyzing data and making knowledge based upon the information, using data mining techniques as an important technology for accurate and efficient data investigation, has increased. Association rule mining is as one of well-known technique in data mining science. This method is used to analyze relations between different items in datasets and discover associations’ rules in data collection as descriptive pattern.
Babor force survey is important research in occupation and Labor branch that is conducted in SCI (Statistical Centre of Iran).The key results of this survey are unemployment rate population and economic participation rate population .Recently the result show that inactive ratio population in society has increased and because of that economic participation rate has decreased. For analyzing the unknown reason, the survey dataset is investigated in order to discover relation between family member’s information and job’s status, economical activities. In this study, patterns exploration of active and inactive population is the main objective, so the Apriori algorithm as a method in association rule mining is used to determine hidden pattern in Labor force survey. The knowledge discovered in this research as descriptive rules could be used for making decision in the statistical analysis.
Keywords: Data mining, association rule technique, labor force survey, economic participation rate.
Full-Text [PDF 288 kb]   (1061 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2015/12/19 | Accepted: 2018/07/7 | Published: 2018/09/25

XML   Persian Abstract   Print

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 27, Issue 2 (3-2017) Back to browse issues page