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Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
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Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
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Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
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URL |
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|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
|
|
Title |
Comprehensive review on Clustering Techniques and its application on High Dimensional Data |
|
Author |
Afroj Alam, Mohd Muqeem, and Sultan Ahmad |
| Citation |
Vol. 21 No. 7 pp. 237-244 |
|
Abstract |
Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy |
|
Keywords |
Data mining, Clustering, K-means, PAM, CLARA, ETL, High-dimensional datasets, curse of dimensionality. |
|
URL |
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Title |
Rate of Waste in Authority Names for the Web of Science Journals among Saudi Universities |
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Author |
Abdullah Al Otaibi and Yaser Mohammad Al Sawy |
| Citation |
Vol. 21 No. 7 pp. 267-272 |
|
Abstract |
The current study aimed at measuring the rate of loss in search results of the actual number of publications in journals indexed by Web of Science when not using the accurate official authority name as indicated by the Ministry of Education. Conducting a search using the authority name does not always yield complete results of all existing publications. Researchers in Saudi universities tend to use up to 10 different random names of universities when searching. This interesting fact has prompted the authors of this paper to conduct a study on the search results of 30 Saudi universities using the authority name as indicated by the Ministry of Education. The statistical analyses revealed that there is a high tendency for the wrong use of authority names. Results show that 8 universities were not found in the search results. Furthermore, other universities are losing between 10 and 30% of search results that reflect the actual number of publications. Consequently, the rank of each university, as well as the general rank of Saudi universities in the Web of Science, will be affected. |
|
Keywords |
Web of Science Ranking; Saudi Universities Authority Names; Web of Science advanced search; Boolean Research; Authority Control. |
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URL |
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Title |
Rate of Waste in Authority Names for the Web of Science Journals among Saudi Universities |
|
Author |
Abdullah Al Otaibi and Yaser Mohammad Al Sawy |
| Citation |
Vol. 21 No. 7 pp. 267-272 |
|
Abstract |
The current study aimed at measuring the rate of loss in search results of the actual number of publications in journals indexed by Web of Science when not using the accurate official authority name as indicated by the Ministry of Education. Conducting a search using the authority name does not always yield complete results of all existing publications. Researchers in Saudi universities tend to use up to 10 different random names of universities when searching. This interesting fact has prompted the authors of this paper to conduct a study on the search results of 30 Saudi universities using the authority name as indicated by the Ministry of Education. The statistical analyses revealed that there is a high tendency for the wrong use of authority names. Results show that 8 universities were not found in the search results. Furthermore, other universities are losing between 10 and 30% of search results that reflect the actual number of publications. Consequently, the rank of each university, as well as the general rank of Saudi universities in the Web of Science, will be affected. |
|
Keywords |
Web of Science Ranking; Saudi Universities Authority Names; Web of Science advanced search; Boolean Research; Authority Control. |
|
URL |
|
|
Title |
Rate of Waste in Authority Names for the Web of Science Journals among Saudi Universities |
|
Author |
Abdullah Al Otaibi and Yaser Mohammad Al Sawy |
| Citation |
Vol. 21 No. 7 pp. 267-272 |
|
Abstract |
The current study aimed at measuring the rate of loss in search results of the actual number of publications in journals indexed by Web of Science when not using the accurate official authority name as indicated by the Ministry of Education. Conducting a search using the authority name does not always yield complete results of all existing publications. Researchers in Saudi universities tend to use up to 10 different random names of universities when searching. This interesting fact has prompted the authors of this paper to conduct a study on the search results of 30 Saudi universities using the authority name as indicated by the Ministry of Education. The statistical analyses revealed that there is a high tendency for the wrong use of authority names. Results show that 8 universities were not found in the search results. Furthermore, other universities are losing between 10 and 30% of search results that reflect the actual number of publications. Consequently, the rank of each university, as well as the general rank of Saudi universities in the Web of Science, will be affected. |
|
Keywords |
Web of Science Ranking; Saudi Universities Authority Names; Web of Science advanced search; Boolean Research; Authority Control. |
|
URL |
