Open AccessProceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- pp 487-499
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This article is published in Very Large Data Bases.The article was published on 1994-09-12 and is currently open access. It has received 10454 citations till now. The article focuses on the topics: Association rule learning & Apriori algorithm.read more
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TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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Fast algorithms for mining association rules
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Mining and summarizing customer reviews
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TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
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TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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Efficient Similarity Search In Sequence Databases
TL;DR: An indexing method for time sequences for processing similarity queries using R * -trees to index the sequences and efficiently answer similarity queries and provides experimental results which show that the method is superior to search based on sequential scanning.
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