Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2018, Cilt: 1 Sayı: 2, 1 - 16, 01.08.2018

Amal Alhatali Arockiasamy Soosaimanickam

Öz

Kaynakça

  • Akash Rajak and Mahendra Kumar Gupta (2012)."Association Rule Mining: Applications in Various Areas", Krishna Institute of Engineering & Technology, Pages 3-7.
  • Alapan, Maribeth P.(2016)."Factors Affecting the Market Price of Fish in the Northern Part of Surigao Del Sur, Philippines",Journal of Environment and Ecology, Issue number: 2,Volume number:7, Pages:34.
  • Altmeyer, Sebastian, and Robert I. Davis. (2014). "On the Correctness, Optimality and Precision of Static Probabilistic Timing Analysis". In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014, Pages 1–6. New Jersey: IEEE Conference Publications. doi:10.7873/DATE.2014.039.
  • Amal Al Mugrashi, Arockiasamy Sosoaimanickam. (2018). “A Comparative Study of the Efficient Data Mining Algorithm for Forecasting Least Prices in Oman Fish Markets”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 11, pp. 8751-8758.
  • Anita Bai (2015). "An Application of Factor Analysis in the Evaluation of Country Economic Rank",Procedia Computer Science, Volume number: 54, Pages:311-317.
  • Cucu-Grosjean, Liliana, Luca Santinelli, Michael Houston, Code Lo, Tullio Vardanega, Leonidas Kosmidis, Jaume Abella, Enrico Mezzetti, Eduardo Quinones, and Francisco J. Cazorla (2012). "Measurement-Based Probabilistic Timing Analysis for Multi-Path Programs. In 2012 24th Euromicro Conference on Real-Time Systems", Pages 91–101. IEEE. doi:10.1109/ECRTS.2012.31.
  • D. Koller and M. Sahami(1996). "Toward optimal feature selection", In Proceedings of the Thirteenth International Conference on Machine Learning, Pages 284–292, 1996.
  • Muscat Newspaper, Article title: "6% rise in fish production; ministry targets half a million tonnes by 2020 – Oman", Website title: Muscat Daily News,URL: https://www.muscatdaily.com/Archive/Oman/6-rise-in-fish-production-ministry-targets-half-a-million-tonnes-by-2020-52dq.
  • Madhu Sudana Rao Nalluri , SaiSujana T , Harshini Reddy K ,Swaminathan V (2017).An Efficient Feature Selection using Artificial Fish Swarm Optimization and SVM Classifier. 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). Pages 20-22,on July 2017
  • Ministry of Agriculture and Fisheries (2016), "أداء القطاع الزراعي والسمكي - المؤشرات الإنتاجية والإقتصادية",Website title: Maf.gov.om,URL: http://www.maf.gov.om/pages/PageCreator.aspx?lang=AR&DId=0&I=0&CId=0&CMSId=800746.
  • Farm and fishery vital for Oman's economy (2014), Avaliable On: http://timesofoman.com/article/31777/Oman/Farm-and-fishery-vital-for-Oman's-economy.
  • Fengyan Fan (2017),"Factor analysis of energy-related carbon emissions: a case study of Beijing,Journal of Cleaner Production ,Volume number: 163, Pages: S277-S283.
  • Fish production in Oman grows by 5.4% (2017), Fish production in Oman grows by 5.4%. Times of Oman. Retrieved 5 September 2017, from http://timesofoman.com/article/112688.
  • G. VamsiKrishna (2015),"An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis",International Journal of Computer Applications, Issue number: (11), Volume number:120, Pages:26-29.
  • H. Almuallim and T. G. Dietterich (1994), "Learning boolean concepts in the presence of many irrelevant features", Artificial Intelligence, vol. 69, no. 1-2, Pages 279–305, 1994.
  • Hacer, E Aykut, E Halil and E Hamit, 2015,"Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models", Journal of Economics and International Finance, Issue number:5, Volume number:7, Pages:127-136.
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2009), "The WEKA data mining software", ACM SIGKDD Explorations Newsletter, vol 11(issue 1), Pages: 10. http://dx.doi.org/10.1145/1656274.1656278.
  • Hu Jing; Yang NingSheng; Ouyang HaiYing; Sun YingZe; Chen BaiSong (2013), "Application progress on data mining in field of fishery production", Journal of Agricultural Science and Technology (Beijing), Vol.15, issue No.4 ,Pages 176-182.
  • Kosmidis, Leonidas, Eduardo Quiñones, Jaume Abella, Tullio Vardanega, Carles Hernandez, Andrea Gianarro, Ian Broster, and Francisco J. Cazorla. (2016), "Fitting Processor Architectures for Measurement-Based Probabilistic Timing Analysis" . Microprocessors and Microsystems 47. Elsevier B.V.: Pages 287–302. doi:10.1016/j.micpro.2016.07.014.
  • Liaw, Andy (2012), Documentation for R package random Forest.Pages 55-60.
  • M.Vasantha & Dr.V.Subbiah Bharathy (2010), "Evaluation of Attribute Selection Methods with Tree based Supervised Classification-A Case Study with Mammogram Images". International Journal of Computer Applications (0975 – 8887) Volume 8– issue No.12, October 2010.
  • Osiris Villacampa (2015), Feature Selection and Classification Methods for Decision Making: A Comparative Analysis.
  • PandyaJalpa P., MorenaRustom D (2017), "A Survey on Association Rule Mining Algorithms Used in Different Application Areas". Volume 8, issue No. 5, May-June 2017, Pages 1430-1436.
  • Qatan, Salim (2013), "Operating a wholesale fish market in the sultanate of Oman analyses of external factors" . Iceland: UNU-Fisheries Training Programme.Pages 40-45.
  • Sudhir , J., & Kodge, B., (2013), WEKA. Census Data Mining and Data Analysis Using WEKA. 1-6.
  • Tao Chen, Chu Zhang and Lifeng Xu (2016), "Factor analysis of fatal road traffic crashes with massive casualties in China", Advances in Mechanical Engineering 2016, Vol. 8(issue no. 4) Pages 1–11.
  • Seyed Jamal F. Hosseini and Nioushah Eghtedari (2013), "A confirmatory factorial analysis affecting the development of nanotechnology in agricultural sector of Iran". African Journal of Agricultural Research. Vol. 8(issue no. 16), Pages 1401-1404.
  • Trupti A. Kumbhare and Prof. Santosh V. Chobe (2014), "An Overview of Association Rule Mining Algorithms", (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (issue no.1) , 2014, Pages 927-930.
  • Usha Ananthakumar & Ankita Mridha (2017). "Application of factor analysis in analyzing sales of the retail stores". Retrieved from https://ieeexplore.ieee.org/document/7919597/
  • W. Duch, T. Winiarski, J. Biesiada, J, and A. Kachel (2003), "Feature Ranking, Selection and Discretization", Int. Conf. on Artificial Neural Networks (ICANN) and Int. Conf. on Neural Information Processing (ICONIP), Pages 251 – 254, 2003.
  • Wohlfarth, T., Clémençon, S., Roueff, F., & Casellato, X. (2011). "A data-mining approach to travel price forecasting", In Machine Learning and Applications and Workshops (ICMLA), 2011 10Th International Conference On, 1, Pages 84-89. URL: https://hal.archivesouvertes. fr/hal-00665041/file/ICMLA.pdf
  • Zahra Karimi (2013). "Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods",International Journal of Computer Applications,Issue number(4),Volume number:78, Pages:21-27.

A Comparative Study of the Efficient Data Mining Algorithm to Find the Most Influenced Factor on Price Variation in Oman Fish Markets

Yıl 2018, Cilt: 1 Sayı: 2, 1 - 16, 01.08.2018

Amal Alhatali Arockiasamy Soosaimanickam

Öz

In oman, Fishing is one of the oldest professions that
provides significantly to the national economy and for creating more job
opportunities, especially, where many people completely depend on this income
as an important source of living. The customers dealing with Fish markets in
Oman need a good and innovative software platform to help them to deal with the
problem of increasing of fish prices. This study aims at analysing various
factors behind increasing prices in Oman fish markets using some data mining
algorithms, by means of studying the old data that kept on the database that
will assist to make a proper decision. The research has been conducted for the
data collected from 29 fish markets in Sultanate of Oman and 15 fish species in
each markets have been considered. To analyse the data, data mining algorithms,
namely J48 algorithm, Decision Stump, and Random Tree has been chosen to
perform the classification of data to find the most affected factor in fish
price variations. The suitable algorithm has been chosen based on the good
performance, which has been used for building an application. The result of the
study shows that the Time is the major factor for price variations followed by
Place and then the quantity. This application model will help customers to get
different information about prices in fish markets in Oman.

Anahtar Kelimeler

Oman Fish Market Factor Analyses Attribute Selection Information Gain Weka software Classification algorithm

Kaynakça

  • Akash Rajak and Mahendra Kumar Gupta (2012)."Association Rule Mining: Applications in Various Areas", Krishna Institute of Engineering & Technology, Pages 3-7.
  • Alapan, Maribeth P.(2016)."Factors Affecting the Market Price of Fish in the Northern Part of Surigao Del Sur, Philippines",Journal of Environment and Ecology, Issue number: 2,Volume number:7, Pages:34.
  • Altmeyer, Sebastian, and Robert I. Davis. (2014). "On the Correctness, Optimality and Precision of Static Probabilistic Timing Analysis". In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014, Pages 1–6. New Jersey: IEEE Conference Publications. doi:10.7873/DATE.2014.039.
  • Amal Al Mugrashi, Arockiasamy Sosoaimanickam. (2018). “A Comparative Study of the Efficient Data Mining Algorithm for Forecasting Least Prices in Oman Fish Markets”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 11, pp. 8751-8758.
  • Anita Bai (2015). "An Application of Factor Analysis in the Evaluation of Country Economic Rank",Procedia Computer Science, Volume number: 54, Pages:311-317.
  • Cucu-Grosjean, Liliana, Luca Santinelli, Michael Houston, Code Lo, Tullio Vardanega, Leonidas Kosmidis, Jaume Abella, Enrico Mezzetti, Eduardo Quinones, and Francisco J. Cazorla (2012). "Measurement-Based Probabilistic Timing Analysis for Multi-Path Programs. In 2012 24th Euromicro Conference on Real-Time Systems", Pages 91–101. IEEE. doi:10.1109/ECRTS.2012.31.
  • D. Koller and M. Sahami(1996). "Toward optimal feature selection", In Proceedings of the Thirteenth International Conference on Machine Learning, Pages 284–292, 1996.
  • Muscat Newspaper, Article title: "6% rise in fish production; ministry targets half a million tonnes by 2020 – Oman", Website title: Muscat Daily News,URL: https://www.muscatdaily.com/Archive/Oman/6-rise-in-fish-production-ministry-targets-half-a-million-tonnes-by-2020-52dq.
  • Madhu Sudana Rao Nalluri , SaiSujana T , Harshini Reddy K ,Swaminathan V (2017).An Efficient Feature Selection using Artificial Fish Swarm Optimization and SVM Classifier. 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). Pages 20-22,on July 2017
  • Ministry of Agriculture and Fisheries (2016), "أداء القطاع الزراعي والسمكي - المؤشرات الإنتاجية والإقتصادية",Website title: Maf.gov.om,URL: http://www.maf.gov.om/pages/PageCreator.aspx?lang=AR&DId=0&I=0&CId=0&CMSId=800746.
  • Farm and fishery vital for Oman's economy (2014), Avaliable On: http://timesofoman.com/article/31777/Oman/Farm-and-fishery-vital-for-Oman's-economy.
  • Fengyan Fan (2017),"Factor analysis of energy-related carbon emissions: a case study of Beijing,Journal of Cleaner Production ,Volume number: 163, Pages: S277-S283.
  • Fish production in Oman grows by 5.4% (2017), Fish production in Oman grows by 5.4%. Times of Oman. Retrieved 5 September 2017, from http://timesofoman.com/article/112688.
  • G. VamsiKrishna (2015),"An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis",International Journal of Computer Applications, Issue number: (11), Volume number:120, Pages:26-29.
  • H. Almuallim and T. G. Dietterich (1994), "Learning boolean concepts in the presence of many irrelevant features", Artificial Intelligence, vol. 69, no. 1-2, Pages 279–305, 1994.
  • Hacer, E Aykut, E Halil and E Hamit, 2015,"Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models", Journal of Economics and International Finance, Issue number:5, Volume number:7, Pages:127-136.
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2009), "The WEKA data mining software", ACM SIGKDD Explorations Newsletter, vol 11(issue 1), Pages: 10. http://dx.doi.org/10.1145/1656274.1656278.
  • Hu Jing; Yang NingSheng; Ouyang HaiYing; Sun YingZe; Chen BaiSong (2013), "Application progress on data mining in field of fishery production", Journal of Agricultural Science and Technology (Beijing), Vol.15, issue No.4 ,Pages 176-182.
  • Kosmidis, Leonidas, Eduardo Quiñones, Jaume Abella, Tullio Vardanega, Carles Hernandez, Andrea Gianarro, Ian Broster, and Francisco J. Cazorla. (2016), "Fitting Processor Architectures for Measurement-Based Probabilistic Timing Analysis" . Microprocessors and Microsystems 47. Elsevier B.V.: Pages 287–302. doi:10.1016/j.micpro.2016.07.014.
  • Liaw, Andy (2012), Documentation for R package random Forest.Pages 55-60.
  • M.Vasantha & Dr.V.Subbiah Bharathy (2010), "Evaluation of Attribute Selection Methods with Tree based Supervised Classification-A Case Study with Mammogram Images". International Journal of Computer Applications (0975 – 8887) Volume 8– issue No.12, October 2010.
  • Osiris Villacampa (2015), Feature Selection and Classification Methods for Decision Making: A Comparative Analysis.
  • PandyaJalpa P., MorenaRustom D (2017), "A Survey on Association Rule Mining Algorithms Used in Different Application Areas". Volume 8, issue No. 5, May-June 2017, Pages 1430-1436.
  • Qatan, Salim (2013), "Operating a wholesale fish market in the sultanate of Oman analyses of external factors" . Iceland: UNU-Fisheries Training Programme.Pages 40-45.
  • Sudhir , J., & Kodge, B., (2013), WEKA. Census Data Mining and Data Analysis Using WEKA. 1-6.
  • Tao Chen, Chu Zhang and Lifeng Xu (2016), "Factor analysis of fatal road traffic crashes with massive casualties in China", Advances in Mechanical Engineering 2016, Vol. 8(issue no. 4) Pages 1–11.
  • Seyed Jamal F. Hosseini and Nioushah Eghtedari (2013), "A confirmatory factorial analysis affecting the development of nanotechnology in agricultural sector of Iran". African Journal of Agricultural Research. Vol. 8(issue no. 16), Pages 1401-1404.
  • Trupti A. Kumbhare and Prof. Santosh V. Chobe (2014), "An Overview of Association Rule Mining Algorithms", (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (issue no.1) , 2014, Pages 927-930.
  • Usha Ananthakumar & Ankita Mridha (2017). "Application of factor analysis in analyzing sales of the retail stores". Retrieved from https://ieeexplore.ieee.org/document/7919597/
  • W. Duch, T. Winiarski, J. Biesiada, J, and A. Kachel (2003), "Feature Ranking, Selection and Discretization", Int. Conf. on Artificial Neural Networks (ICANN) and Int. Conf. on Neural Information Processing (ICONIP), Pages 251 – 254, 2003.
  • Wohlfarth, T., Clémençon, S., Roueff, F., & Casellato, X. (2011). "A data-mining approach to travel price forecasting", In Machine Learning and Applications and Workshops (ICMLA), 2011 10Th International Conference On, 1, Pages 84-89. URL: https://hal.archivesouvertes. fr/hal-00665041/file/ICMLA.pdf
  • Zahra Karimi (2013). "Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods",International Journal of Computer Applications,Issue number(4),Volume number:78, Pages:21-27.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Amal Alhatali 0000-0002-6821-9397

Arockiasamy Soosaimanickam

Yayımlanma Tarihi 1 Ağustos 2018
Gönderilme Tarihi 23 Haziran 2018
Kabul Tarihi 2 Ağustos 2018
Yayımlandığı Sayı Yıl 2018Cilt: 1 Sayı: 2

Kaynak Göster

IEEE A. Alhatali ve A. Soosaimanickam, “A Comparative Study of the Efficient Data Mining Algorithm to Find the Most Influenced Factor on Price Variation in Oman Fish Markets”, SAUCIS, c. 1, sy. 2, ss. 1–16, 2018.
 Kapak Resmi İndir

    Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science