Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kanak Meena"'
Publikováno v:
Applied Computing and Informatics, Vol 18, Iss 1/2, Pp 22-44 (2022)
The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load im
Externí odkaz:
https://doaj.org/article/fb2a04e4855e48448173ad00e14569eb
Publikováno v:
Applied Computing and Informatics. 18:22-44
The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::232bc52b5ae6523a804a85b3b4ecc2ff
https://doi.org/10.1016/j.aci.2018.11.001
https://doi.org/10.1016/j.aci.2018.11.001
Autor:
Devendra K. Tayal, Kanak Meena
Publikováno v:
Future Generation Computer Systems. 103:44-57
Associative classification is a promising methodology in information mining that uses the association rule discovery procedures to build the classifier. But they have some limitations like: they are not able to handle big data as they have memory con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7bdc39587d9879510cdb3aa29bdf105
https://doi.org/10.1016/j.future.2019.09.040
https://doi.org/10.1016/j.future.2019.09.040
Publikováno v:
Artificial Intelligence in Medicine. 94:138-152
Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a datab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a1b5b1793e07c2be38aa6684539b0e0
https://doi.org/10.1016/j.artmed.2019.02.005
https://doi.org/10.1016/j.artmed.2019.02.005
Autor:
Nikita Jain, Kanak Meena
Publikováno v:
International Journal on Computer Science and Engineering. 10:71-76
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92291ff907e91fb0bb7e81f6cb59347c
https://doi.org/10.21817/ijcse/2018/v10i3/181003010
https://doi.org/10.21817/ijcse/2018/v10i3/181003010
Publikováno v:
2018 3rd International Conference on Communication and Electronics Systems (ICCES).
Data Mining is a technique of using available information of an entity to extract useful patterns from the large database which can be implemented in various sectors. It is an essential process where intelligent methods are applied to extract relatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a3c722bd1ee71de95e4f1ee7a7f6330
https://doi.org/10.1109/cesys.2018.8724082
https://doi.org/10.1109/cesys.2018.8724082
Autor:
Devendra K. Tayal, Kanak Meena
Publikováno v:
Proceedings of 2nd International Conference on Communication, Computing and Networking ISBN: 9789811312168
Uneven distribution of data between the nodes causes the data skewness problem. Due to this problem, various problems occur during the processing. So, this paper presents the brief analysis of the existing techniques related to load imbalancing with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c41890b4edfdd1cc09502b85e68508cd
https://doi.org/10.1007/978-981-13-1217-5_92
https://doi.org/10.1007/978-981-13-1217-5_92
Autor:
Kanak Meena, Devendra K. Tayal
Publikováno v:
2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
In this paper, we have proposed a partitioning technique to handle big data. MapReduce is very sensitive to data skewness so there is a need to develop such technique which handle the data skewness and do not affect the performance of the task. To ov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7aad8e0e37f2bf03995b0de5c148cfe9
https://doi.org/10.1109/icicct.2018.8473202
https://doi.org/10.1109/icicct.2018.8473202