Path: Top -> Journal -> Telkomnika -> 2016 -> Vol 14, No 3A
Analysis on a New Data Mining Algorithm of the Statistics Work
Analysis on a New Data Mining Algorithm of the Statistics Work
Journal from gdlhub / 2016-11-11 02:41:19Oleh : Ding Zhi, Ha Yan, Li Yan, Telkomnika
Dibuat : 2016-09-01, dengan 1 file
Keyword : Statistical information; Data mining; Algorithm
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4383
The data mining is a massive, incomplete and random data. People don't know how to extract the data in advance because of the large amount of data of the network in the database, and the heterogeneous and dynamic characteristic, which makes the extraction process difficult. Data mining needs effective calculation. Cloud computing, as a business computing model, can assign a large number of calculate tasks. Based on usersÂ’ need, it can allocate the computing, storage and application ability, and can solve massive computing problems effectively in data mining. According to the sequence of item location and the growth algorithm of SPM - LIPI mining, the algorithm discussed in this paper can improve the traditional algorithm, and form the same item at the end of the sequence position information table. It can also avoid repeated scans of the database mining through trimming by the database.
Deskripsi Alternatif :The data mining is a massive, incomplete and random data. People don't know how to extract the data in advance because of the large amount of data of the network in the database, and the heterogeneous and dynamic characteristic, which makes the extraction process difficult. Data mining needs effective calculation. Cloud computing, as a business computing model, can assign a large number of calculate tasks. Based on usersÂ’ need, it can allocate the computing, storage and application ability, and can solve massive computing problems effectively in data mining. According to the sequence of item location and the growth algorithm of SPM - LIPI mining, the algorithm discussed in this paper can improve the traditional algorithm, and form the same item at the end of the sequence position information table. It can also avoid repeated scans of the database mining through trimming by the database.
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