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

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.