research-article

Intelligent Data Analysis using Optimized Support Vector Machine Based Data Mining Approach for Tourism Industry

Authors:

Ms Promila Sharma,

Girish Kumar SharmaAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 5

Article No.: 94, Pages 1 - 20

https://doi.org/10.1145/3494566

Published: 09 March 2022 Publication History

Abstract

Data analysis involves the deployment of sophisticated approaches from data mining methods, information theory, and artificial intelligence in various fields like tourism, hospitality, and so on for the extraction of knowledge from the gathered and preprocessed data. In tourism, pattern analysis or data analysis using classification is significant for finding the patterns that represent new and potentially useful information or knowledge about the destination and other data. Several data mining techniques are introduced for the classification of data or patterns. However, overfitting, less accuracy, local minima, sensitive to noise are the drawbacks in some existing data mining classification methods. To overcome these challenges, Support vector machine with Red deer optimization (SVM-RDO) based data mining strategy is proposed in this article. Extended Kalman filter (EKF) is utilized in the first phase, i.e., data cleaning to remove the noise and missing values from the input data. Mantaray foraging algorithm (MaFA) is used in the data selection phase, in which the significant data are selected for the further process to reduce the computational complexity. The final phase is the classification, in which SVM-RDO is proposed to access the useful pattern from the selected data. PYTHON is the implementation tool used for the experiment of the proposed model. The experimental analysis is done to show the efficacy of the proposed work. From the experimental results, the proposed SVM-RDO achieved better accuracy, precision, recall, and F1 score than the existing methods for the tourism dataset. Thus, it is showed the effectiveness of the proposed SVM-RDO for pattern analysis.

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Cited By

Xu R(2023)Framework for Building Smart Tourism Big Data Mining Model for Sustainable DevelopmentSustainability10.3390/su1506516215:6(5162)Online publication date: 14-Mar-2023
https://doi.org/10.3390/su15065162
Huang J(2023)Construction of an Intelligent Tourism Public Service Platform Featuring Communication Big data and Blockchain2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS)10.1109/ICIICS59993.2023.10420928(1-7)Online publication date: 24-Nov-2023
https://doi.org/10.1109/ICIICS59993.2023.10420928
Dalipi FKastrati ZÖberg T(2023)The Impact of Artificial Intelligence on Tourism Sustainability: A Systematic Mapping Review2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)10.1109/ICCIKE58312.2023.10131818(119-125)Online publication date: 9-Mar-2023
https://doi.org/10.1109/ICCIKE58312.2023.10131818
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Index Terms

Intelligent Data Analysis using Optimized Support Vector Machine Based Data Mining Approach for Tourism Industry
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Kernel methods
        Support vector machines
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 5

October 2022

532 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3514187

Editor:
Charu Aggarwal
IBM T.J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2022

Accepted: 01 October 2021

Revised: 01 October 2021

Received: 01 July 2021

Published in TKDD Volume 16, Issue 5

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Cited By

Xu R(2023)Framework for Building Smart Tourism Big Data Mining Model for Sustainable DevelopmentSustainability10.3390/su1506516215:6(5162)Online publication date: 14-Mar-2023
https://doi.org/10.3390/su15065162
Huang J(2023)Construction of an Intelligent Tourism Public Service Platform Featuring Communication Big data and Blockchain2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS)10.1109/ICIICS59993.2023.10420928(1-7)Online publication date: 24-Nov-2023
https://doi.org/10.1109/ICIICS59993.2023.10420928
Dalipi FKastrati ZÖberg T(2023)The Impact of Artificial Intelligence on Tourism Sustainability: A Systematic Mapping Review2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)10.1109/ICCIKE58312.2023.10131818(119-125)Online publication date: 9-Mar-2023
https://doi.org/10.1109/ICCIKE58312.2023.10131818
Mor MDalyot SRam Y(2023)Who is a tourist? Classifying international urban tourists using machine learningTourism Management10.1016/j.tourman.2022.10468995(104689)Online publication date: Apr-2023
https://doi.org/10.1016/j.tourman.2022.104689
Xiong WXu GLi YZhang FYe PLi B(2023)Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curvesJournal of Energy Storage10.1016/j.est.2023.10679062(106790)Online publication date: Jun-2023
https://doi.org/10.1016/j.est.2023.106790
Zhang Y(2022)Intelligent Mining and Visualization of Massive Financial Digital Archives Based on Attribute Grouping2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)10.1109/ICKECS56523.2022.10059688(1-5)Online publication date: 28-Dec-2022
https://doi.org/10.1109/ICKECS56523.2022.10059688

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