Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Yaling Xun"'
Publikováno v:
IEEE Access, Vol 8, Pp 97986-98000 (2020)
The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with Hα emission line from massive data are necessary for the further study, while the most of
Externí odkaz:
https://doaj.org/article/7f525be175e245a7a8d4d70f5ad4861d
Publikováno v:
IEEE Access, Vol 7, Pp 136511-136524 (2019)
Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression
Externí odkaz:
https://doaj.org/article/1ff8f98f43e349ccb8abadc3160c6c2b
Publikováno v:
Information Sciences. 615:638-656
Publikováno v:
Computer Supported Cooperative Work and Social Computing ISBN: 9789819923557
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f66ceaa069b8c2272a9d5516862abbf1
https://doi.org/10.1007/978-981-99-2356-4_22
https://doi.org/10.1007/978-981-99-2356-4_22
Publikováno v:
Applied Intelligence. 51:991-1009
Discretization is one of the data preprocessing topics in the field of data mining, and is a critical issue to improve the efficiency and quality of data mining. Multi-scale can reveal the structure and hierarchical characteristics of data objects, t
Publikováno v:
IEEE Access, Vol 8, Pp 97986-98000 (2020)
The $\text{H}\alpha $ emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with $\text{H}\alpha $ emission line from massive data are necessary for the furt
Publikováno v:
Information Sciences. 504:1-19
This paper proposes a feature-grouping based parallel outlier mining method called POS for high-dimensional categorical datasets. Existing methods of outlier mining are inadequate to deal with datasets which are so voluminous and complex. We solve th
Publikováno v:
Parallel Computing. 101:102738
The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. Facing big data applications, parallel and distributed solutions are widely studied. However, the frequent items
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 28:101-114
Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally partitioning data among a group of computing nodes. We start this study by discovering a serious performance problem of the existing parallel Frequent Itemset