scholarly journals Analyzing the relationship between information technology jobs advertised on-line and skills requirements using association rules

2021 ◽  
Vol 10 (5) ◽  
pp. 2771-2779
Author(s):  
Frederick F. Patacsil ◽  
Michael Acosta
Keyword(s):  
Labor Market ◽  
Association Rule ◽  
Market Demand ◽  
Rule Mining ◽  
It Industry ◽  
Job Vacancy ◽  
On Line ◽  
Philippine Government ◽  
The Relationship ◽  
Source Of Information

Online job vacancy sites have become an important source of information about the characteristics of labor market demand. It has become an avenue for job matching by both employers and employees and to study and analyze the labor market. This study proposed a methodology for identifying and analyzing skill-job relationships using frequency word occurrences of skills as a requirement of the job. It employed association rule mining which aims to discover frequent patterns, relationships among a set of items in the database. It collected published job vacancy data to IT job and skills requirements from various job portal websites. The proposed job skill requirements for specific I.T. jobs published online analyzing using the FP-growth algorithm of association rule provide a new dimension in labor market research. The study revealed that skill words are highly related to a certain job requirement. The results of the study could provide insights on the gap between the school acquired skills and actual IT industry skill needs and as the basis for curriculum enhancement and policy-making interventions by the Philippine government in its educational system.

scholarly journals Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Ming-Hseng Tseng ◽  
Hui-Ching Wu
Keyword(s):  
Association Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Immigrant Women ◽  
Parental Knowledge ◽  
Physical Factors ◽  
Rule Mining ◽  
Socioeconomic Characteristics ◽  
Healthcare Needs ◽  
The Relationship

Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.

A Fault Diagnosis Model for Power Transformer Using Association Rule Mining-Based on Rough Set

2014 ◽  
Vol 519-520 ◽  
pp. 1169-1172
Author(s):  
De Wen Wang ◽  
Lin Xiao He
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Power Transformer ◽  
Monitoring Data ◽  
Rule Mining ◽  
Monitoring Technology ◽  
On Line ◽  
Diagnosis Model

With the development of on-line monitoring technology of electric power equipment, and the accumulation of both on-line monitoring data and off-line testing data, the data available to fault diagnosis of power transformer is bound to be massive. How to utilize those massive data reasonably is the issue that eagerly needs us to study. Since the on-line monitoring technology is not totally mature, which resulting in incomplete, noisy, wrong characters for monitoring data, so processing the initial data by using rough set is necessary. Furthermore, when the issue scale becomes larger, the computing amount of association rule mining grows dramatically, and its easy to cause data expansion. So it needs to use attribute reduction algorithm of rough set theory. Taking the above two points into account, this paper proposes a fault diagnosis model for power transformer using association rule mining-based on rough set.

The Traffic Accidents Analysis Base on Association Rule Mining of Yuxi City in China

2014 ◽  
Vol 1030-1032 ◽  
pp. 2161-2165
Author(s):  
Shao Yun Song
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Traffic Accidents ◽  
Relationship Matrix ◽  
Rule Mining ◽  
Small Cities ◽  
Accident Data ◽  
Analysis System ◽  
The Relationship

According to the characteristics of small cities accident in China, selectively build data mining models. The algorithm focus on mining association rules to small cities accidents analysis system. Experiments show that the algorithm is superior to other algorithms. In this paper, the relationship matrix algorithm by using association rules on accident data, data mining, and mining results were analyzed to verify the effectiveness of the system.

scholarly journals Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yang Ou ◽  
Zheng Jiang Liu ◽  
Hamid Reza Karimi ◽  
Ying Tian
Keyword(s):  
Genetic Algorithm ◽  
Resource Management ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Immune Genetic Algorithm ◽  
Coding Scheme ◽  
The Relationship ◽  
Multilevel Association Rules

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Immigration and the Structure of Demand: Do Immigrants Alter the Labor Market Composition of U.S. Cities?

2002 ◽  
Vol 36 (1) ◽  
pp. 58-80 ◽  
Author(s):  
April Linton
Keyword(s):  
Labor Market ◽  
Metropolitan Areas ◽  
City Size ◽  
Market Demand ◽  
Level Of Education ◽  
Recent Immigrants ◽  
Occupational Distribution ◽  
Market Composition ◽  
The Relationship ◽  
American Cities

Does the presence of immigrants help determine the types of jobs that exist in American cities and the size of various sectors of these cities' economies? This study explores the relationship between immigration and labor market demand in U.S. metropolitan areas. I employ information about the occupational distribution of recent immigrants as compared to natives to analyze the circumstances under which the two groups are more likely to compete with or complement each other. The findings lend qualified support to Light and Rosenstein's (1995) specific demand hypothesis: many immigrants fill occupational niches that would not exist in their absence. The strength of this conclusion is contingent on city size and immigrants' level of education.

scholarly journals Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246233
Author(s):  
Suelane Garcia Fontes ◽  
Ronaldo Gonçalves Morato ◽  
Silvio Luiz Stanzani ◽  
Pedro Luiz Pizzigatti Corrêa
Keyword(s):  
Social Interactions ◽  
Animal Behavior ◽  
Association Rule ◽  
Association Rule Mining ◽  
Animal Movement ◽  
Behavioral Patterns ◽  
Rule Mining ◽  
Movement Data ◽  
Statistical Measures ◽  
The Relationship

Animal movement data are widely collected with devices such as sensors and collars, increasing the ability of researchers to monitor animal movement and providing information about animal behavioral patterns. Animal behavior is used as a basis for understanding the relationship between animals and the environment and for guiding decision-making by researchers and public agencies about environmental preservation and conservation actions. Animal movement and behavior are widely studied with a focus on identifying behavioral patterns, such as, animal group formation, the distance between animals and their home range. However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil. This allowed us to describe jaguar behavior, social interactions among jaguars and their behavior in different landscapes, thus providing a highly detailed investigation of jaguar movement decisions at the fine scale.

scholarly journals Sistem Rekomendasi Penjualan Menu Makanan di UMKM Kuliner Menggunakan Association Rule

2021 ◽  
Vol 19 (2) ◽  
pp. 87-90
Author(s):  
Ade Kania Ningsih ◽  
Wina Witanti
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Apriori Algorithm ◽  
Process Data ◽  
Rule Mining ◽  
Confidence Threshold ◽  
Support Threshold ◽  
On Line

Micro, Small and Medium Enterprises (MSMEs) are one of the driving motors of the economy in the country, even MSMEs are the backbone of the Economy in Indonesia. MSMEs in Indonesia account for about 60% of GDP (Gross Domestic Product) and also provide employment opportunities to the community. However, with the emergence of THE COVID-19 outbreak of MSMEs in West Java there has been a decrease of up to 80%. This is a problem that exists, MSMEs customers are segmented based on the region due to large-scale social restrictions. This research conducted a review of product sales recommendation system in on-line shop using association rule mining in the culinary industry sector. The research begins with data selection, pre-process data, and data transformation, then the data that has been cleaned will be tested with A priori algorithm. The rules will evaluate using support, confidence, and an upgrade value to determine whether it's the best rule or not. The results of this study are software that will calculate the formation of association rules between culinary products. After an experiment with data amounting to 100 data, an association rule was obtained in the form of a certain pattern of customer behavior, by using Association Rules Technique and Apriori Algorithm, 12 rules are generated with a support threshold of 5% and a confidence threshold of 80%.  , Usaha Kecil dan Menengah (UMKM) merupakan salah satu motor penggerak perekonomian dalam negeri, bahkan UMKM merupakan tulang punggung Perekonomian di Indonesia. UMKM di Indonesia menyumbang sekitar 60% dari PDB (Produk Domestik Bruto) dan juga memberikan kesempatan kerja kepada masyarakat. Namun dengan munculnya Wabah COVID-19 pada UMKM di Jawa Barat terjadi penurunan hingga 80%. Hal ini menjadi permasalahan yang ada, nasabah UMKM tersegmentasi berdasarkan wilayah karena adanya pembatasan sosial berskala besar. Penelitian ini melakukan review terhadap sistem rekomendasi penjualan produk di toko on-line dengan menggunakan Association rule mining pada sektor industri kuliner. Penelitian diawali dengan pemilihan data, data praproses, dan transformasi data, kemudian data yang telah dibersihkan akan diuji dengan algoritma apriori. Aturan akan mengevaluasi menggunakan dukungan, keyakinan, dan nilai peningkatan untuk menentukan apakah itu aturan terbaik atau bukan. Hasil dari penelitian ini berupa software yang akan menghitung pembentukan aturan asosiasi antar produk kuliner. Setelah dilakukan percobaan dengan data sebanyak 100 data, diperoleh aturan asosiasi berupa pola perilaku konsumen tertentu, dengan menggunakan Association Rules Technique dan Apriori Algorithm dihasilkan 12 aturan dengan support threshold 5% dan confidence threshold. dari 80%. 

scholarly journals Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

2017 ◽  
Vol 6 (3) ◽  
pp. 250-255
Author(s):  
Razia Sulthana A ◽  
Subburaj Ramasamy
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
The Internet ◽  
Fuzzy Logics ◽  
Rule Mining ◽  
Customer Reviews ◽  
The Relationship ◽  
New Framework

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.

Sign in / Sign up

Export Citation Format

Share Document