Environmental Examination of Data Mining Algorithms Based on Cloud-Computing

2013 ◽  
Vol 380-384 ◽  
pp. 2911-2914
Author(s):  
Yi Zhuo Guo ◽  
Tao Dai
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cluster Computing ◽  
Programming Model ◽  
Test System ◽  
Parallel Execution ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Parallel Programming Model ◽  
Mining Algorithms

This article on cloud computing and data mining to a more comprehensive study to introduce the concept of cloud computing and data mining, pointed out that the traditional data mining techniques in the case of network test system of massive data mining, processing speed is slow, the load is not balancing and node efficiency is not high enough, Apriori algorithm based on the Map/Reduce parallel programming model, the distributed nature of cloud computing environments, make full use of cluster computing resources to support the parallel execution of algorithms by examples of cloud computing after Apriori algorithm in cloud computing environment to get higher efficiency of frequent itemsets mining algorithm performance than traditional data mining.

The Development of Data Mining System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2280-2283
Author(s):  
Qiong Ren
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Current Data ◽  
Mining System ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Cloud Computing Platform ◽  
Data Mining System ◽  
Process Cost ◽  
Mining Algorithms

With the increasing of input data size, process cost will be very long, for the explosive growth of the Internet data even reached the point of single machine can handle. This article mainly introduces the architecture of the concept of cloud computing and, the mainstream of the analysis of the current data mining algorithms, based on cloud computing to develop the data mining system, providing the operation feasibility of data mining in cloud computing platform, having strong guiding significance.

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

scholarly journals Algoritma Apriori Untuk Penentuan Assosiasi Penjualan Barang

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Calvin Ivan Wiryawan ◽  
Yustina Retno Wahyu Utami ◽  
Didik Nugroho
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Basic Needs ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Growing Up ◽  
Minimum Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The increasing of selling basic needs make the company has to provide a lot of goods. The data will be growing up with increasing the transaction at Sari Bumi store. All this time, the selling basic needs at Sari Bumi Store unstructured well so that needed an application with produce important information that can decide marketing strategies. In this research, Apriori algorithm is used to determine association rules. This method was chosen because it is one of the classic data mining algorithms to look for patterns of relationships between one or more items in one dataset. A priori algorithms can help companies in developing marketing strategies. The result of this research is combination between 4 item set with a minimum support of 30% and minimum confidence of 60%.Keywords: sale, staple, apriori algorithm

scholarly journals DATA MINING ALGORITHM BASED ON CLOUD COMPUTING

2018 ◽  
Vol 48 (4) ◽  
pp. 281-285
Author(s):  
Y. J. HAO
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cellular Neural Network ◽  
Data Mining Algorithm ◽  
Computing Technology ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Hash Algorithm ◽  
Mining Algorithms ◽  
Big Data Technology

The data mining algorithm based on cloud computing is studied and analyzed in this paper. Firstly, the research status and background of the data mining algorithms based on cloud computing are introduced briefly. Secondly, the design of Hash algorithm under cellular neural network is introduced which is needed in this paper. Next, the design of wavelet data compression algorithm for wireless sensor networks is described. Finally, the experimental results and the optimization similarity analysis are obtained. The analysis results show that the data mining algorithm based on cloud computing constructed in this paper plays an important role in data mining, and can improve the data mining algorithm of cloud computing and the development level of cloud computing technology and big data technology to some extent.

scholarly journals Recommendation System for Movie Cast and Crew using Datamining Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1495-1497
Keyword(s):  
Data Mining ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
The Right ◽  
Mining Algorithms

In this article, the data mining algorithms like apriori algorithm is used to suggest the best cast and crew to make a particular genre of movies so that the movie is successful. The data of cast and crew is extracted for which the users have given high average ratings and apply apriori algorithm to give recommendation. There are recommendation systems which gives the recommendation to the users so that the users can watch movies in which they are interested in. But there are no recommendation systems which gives the right information which is helpful for movie making. It is important to make good movies so that the viewers and entertained by it due to which the profit of the producers increases. This research work can be used by the movie makers to select the best cast and crew for the particular genre of movie.

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