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Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
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Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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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Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
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URL |
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|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
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|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 233-240 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 6 pp. 207-212 |
|
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. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
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URL |
