To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 pp. 267-272 |
|
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. 6 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. 6 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. 6 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. 6 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. 6 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. 6 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 |
