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Scalable Frequent Itemset Mining Using Heterogeneous Computing: Parapriori Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Meshram, B. B. |
| Copyright Year | 2014 |
| Abstract | Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent itemsets in large volumes of data in order to produce summarized models of mined rules. These models are extended to generate association rules in various applications such as e-commerce, bio-informatics, associations between image contents and non image features, analysis of effectiveness of sales and retail industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a very short period of time. In the case of increasing data, the time taken to process the data should be almost constant. Since high performance computing has many processors, and many cores, consistent runtime performance for such very large databases on association rules mining is achieved. We, therefore, must rely on high performance parallel and/or distributed computing. In literature survey, we have studied the sequential Apriori algorithms and identified the fundamental problems in sequential environment and parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm improved the computing time, consistency in performance over the increasing load. The empirical analysis of the algorithm also shows that efficiency and scalability is verified over the series of datasets experimented on many core GPU platform. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://airccse.org/journal/ijdps/papers/5514ijdps02.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Apriori algorithm Association rule learning Bioinformatics British Informatics Olympiad Central processing unit Computation (action) Data mining Distributed computing E-commerce General-purpose computing on graphics processing units Genetic Heterogeneity Graphics processing unit Heterogeneous computing Informatics (discipline) Manycore processor Mental association MinEd Multi-core processor Parallel algorithm Published Database Rule (guideline) Run time (program lifecycle phase) Scalability Supercomputer Text mining contents - HtmlLinkType |
| Content Type | Text |
| Resource Type | Article |
