TrORF: building trading areas around organizations based on machine learning techniques

Yuwei Xia, Min Fu, Xi Zheng, Binqing Wu, Zhihao Ke

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Nowadays, making investments in trading areas around organizations is becoming increasingly pervasive since more and more organizations (e.g. universities) have been relocated in suburban-districts far from downtown. It generally requires a fairly long time for investors to make decisions and establish mature trading areas around organizations. Existing researches that focus on optimal location selection ignore the latent relationship between organizations and their surrounding business types. Therefore, this paper proposes a machine-learning based methodology of determining business types and trading areas, named TrORF, to help investors select business types and build trading areas around organizations. We implement our approach and evaluate it by using 13 real-world universities as our case studies. We compare the business type recommendation accuracy of the 13 real-world universities between our method and the other four existing methods. The experiment results indicate that our approach has strong ability to build the suitable business types around each university's trading area since its average recommendation accuracy is 98.23%, and it improves on other approaches by at least 13%.
Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019
EditorsYun Bai, Diego Cabrera, Qibing Yu, Ziqiang Pu
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages118-123
Number of pages6
ISBN (Print)9781728161068
DOIs
Publication statusPublished - 2019
EventInternational Conference on Intelligent Computing, Automation and Systems 2019 - Chongqing, China
Duration: 6 Dec 20198 Dec 2019

Conference

ConferenceInternational Conference on Intelligent Computing, Automation and Systems 2019
Abbreviated titleICICAS 2019
CountryChina
CityChongqing
Period6/12/198/12/19

Keywords

  • investment
  • learning (artificial intelligence)
  • building trading areas
  • organizations
  • mature trading areas
  • machine-learning based methodology
  • real-world universities
  • business type recommendation accuracy
  • university
  • TrORF
  • Machine Learning, Data Mining, Trading Area Building, Business Types Determination
  • Business types determination
  • Data mining
  • Machine learning
  • Trading area building

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