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12 articles for “frequent item set”
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Parallelizing Apriori Algorithm using Open MP
Abstract: An Association rule mining used for finding frequent item sets thus generating rules from the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and computing resources utilization. Apriori Algorithm is used for high dimensionality on massively large data sets. Parallelism reduces the time required for serial processing. Parallel computing can be applied for mining of association rules. Parallel apriori algorithm focus on parallelizing the …
Published in Recent Trends in Parallel Computing · Vol. 1, Issue 1, 2014 · pp. 1–5 Read article
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Survey on Item Set Mining Algorithms
Abstract: In this paper, we have a tendency to gift a literature survey of existing frequent item set mining algorithms. The idea of frequent item set mining is additionally mentioned briefly. The working procedure of some fashionable frequent item set mining techniques is given. Conjointly the deserves and demerits of each method are described. It’s found that the frequent item set mining remains a burning analysis topic. Cite this Article:Aakash Sahu. …
Published in Journal of Advances in Shell Programming · Vol. 2, Issue 1, 2015 · pp. 7–10 Read article
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Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set
Abstract: Association rule learning is a trendy process for discovering exciting relationships between variables in big database. It is frequently used in market basket analysis field e.g. if a buyer buys onions and potatoes then he also purchases beef. But, in fact, it can be implemented in different application area where we want to determine the association among variables. The APRIORI method is definitely the trendiest. But, even with its good …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 3, Issue 2, 2016 · pp. 20–25 Read article
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Remote Sensing Classification Based on Improve Ant-Miner Algorithm: A Case Study of Alwar, Rajasthan, India
Abstract: Earth Observation Satellite (EOS) image itself contains image ambiguity. Various conventional methods like minimum distance to mean or maximum likelihood image clustering algorithm do not meet the accuracy that have required by user in the virtue of cost-effective land use/land cover classification. In Ant colony optimization (ACO), association rule mining is a prevalent and well researched method for discovering useful relations between variables in large databases. The proposed work presents …
Published in Journal of Geotechnical Engineering · Vol. 2, Issue 3, 2015 · pp. 1–5 Read article
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A Comparison Between Two Association Rule Mining Techniques
Abstract: Normal 0 false false false EN-US X-NONE X-NONE Data mining requires associated items to be mined from given transactions. Association rule mining (ARM) has gained importance in view of trend-prediction and decision-making process. Mostly ARM is implemented using variants of FP tree growth techniques. This paper introduces an improved technique for ARM – Improved Relative Dotted Sequence Path (IRDSP)-based ARM. The logic justifying why this technique saves time in comparison …
Published in Current Trends in Information Technology · Vol. 1, Issue 2 - 3, 2011 Read article
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Implementation of MRPrePost Parallel Algorithm based on Hadoop Platform for Large-Data Mining
Abstract: AbstractThe volume, velocity and variety of the data have increased several folds in the past few years. The conventional algorithms and techniques used to mine such huge data are found to be less efficient because these algorithms consider only the large threshold value due to which the number of candidates can be reduced, but this will lead mining association rules production to be inaccurate due to low utilization of data. …
Published in Recent Trends in Parallel Computing · Vol. 4, Issue 2, 2017 · pp. 10–20 Read article
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A Novel Approach for Enhancing Direct Hashing and Pruning for Association Rule Mining
Abstract: ABSTRACTData Mining has been considered as a promising field in the intersection of databases, artificial intelligence and machine learning. Association rule mining has been one of the most popular data mining subjects, which can be simply defined as finding interesting rules from large collections of data. This paper introduces an enhanced hashing approach in discovering associations for largeitemsets. The proposed hashing approach scans the entire database only once using the …
Published in Journal of Computer Technology & Applications · Vol. 3, Issue 1, 2012 · pp. 1–8 Read article
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Web Mining Competitors Analyses for Frequent Unstructured Dataset Using Pattern Mining Utility Incremental Ranking Results Based on Query Process
Abstract: Abstract Nowadays, the mining competition in the market requires best approach fir every company to distinguish not only which companies are its primary competitors but also in whichdomains the company’s rivals compete with itself and what its competitors’ strength is in a specific competitive domain. The task ofcompetitor mining that we address in the paper includes mining all the information such as competitors, competing domains, andcompetitors’ strengthproblems which potential privacy …
Published in Journal of Web Engineering & Technology · Vol. 5, Issue 3, 2018 · pp. 38–46 Read article
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Collocation Pattern Analysis
Abstract: Spatial data mining becomes more attractive and significant as more spatial data is built up in spatial databases. Many GIS applications are using spatial patterns that are equal to association rules of a business data mining, i.e., online transaction processing (OLTP). Mining the spatial collocation patterns is a significant spatial data mining job with broad applications. Organizations having large data sets of spatial data need to do certain operations that …
Published in Journal of Operating Systems Development & Trends · Vol. 1, Issue 1, 2014 · pp. 21–28 Read article
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A Study of Mining High Utility Itemset
Abstract: Cost effective exploitation of a transactional database refers to the procedure of choosing the transaction sets with most cost effective features that will improve overall incomes of a company. A plethora of data mining algorithms have been recommended in the past few years to focus on mining item sets with high utility value. The word utility is about that feature of item-sets. Thus, mining algorithms tries to find out all …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 2, Issue 3, 2015 · pp. 21–25 Read article
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A Review on Effect of Cryogenic Treatment on Wear Resistant Bearing Materials for Automotive Application
Abstract: Cryogenic processing has come as a successful supplementary process to common heat treatment, especially in improving the wear-resistant performance of materials employed in in-vehicle applications. This sub-zero processing is normally performed following quenching and before tempering, during which materials are subjected to extremely low temperatures, typically around -196°C, for prolonged periods of 24 hours. Cryogenic treatment, which involves subjecting materials to extremely low temperatures, has demonstrated promising results in changing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1–11 Read article
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A Novel Clustering Techniques Evaluation using Monte Carlo Simulation
Abstract: In Machine Learning clustering is one the most significant method. Today, we have data in rich from many sources but in order to get meaningful information from it is very boring task. Machine learning clustering algorithms to create cluster to decode the meaningful information from the data, this analysis approach has gained much popularity in recent years. This paper explores evaluation performance of frequently used existing clustering techniques such as …
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 3, 2021 · pp. 1–23 Read article