International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset

  • AlBatati, Fawaz (Umm Al-Qura University, College of Computer and Information Systems, Department of Computer Science) ;
  • Alarabi, Louai (Umm Al-Qura University, College of Computer and Information Systems, Department of Computer Science)
  • Received : 2021.06.05
  • Published : 2021.06.30
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Abstract

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

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References

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