Results 211 to 220 of about 17,882 (265)

Exception rules in association rule mining

Applied Mathematics and Computation, 2008
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Taniar, David., Lee, Vincent., Daly, Olena., Rahayu, Wenny.   +3 more
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MANET Mining: Mining Temporal Association Rules

2008 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2008
A wireless ad hoc network (MANET) is a collection of autonomous nodes or terminals that communicate with each other by forming a multihop radio network and maintaining connectivity in a decentralized manner. MANET is characterized by a rapidly changed topology. As a result packets select different multi-hops paths to reach their destinations.
Ahmad Jabas, Rama Murtty Garimella, Sirandas Ramachandram   +2 more
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MANET mining: Mining step association rules

2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2008
Mobile ad hoc network (MANET) is a collection of autonomous nodes or terminals that communicate with each other by forming a multihop radio network. These nodes maintain connectivity in a decentralized manner. MANET is characterized by a rapidly changed topology. As a result packets select different multi-hops paths to reach their destinations.
Ahmad Jabas, Rama M. Garimella, S. Ramachandram   +2 more
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Mining fuzzy association rules

Proceedings of the sixth international conference on Information and knowledge management, 1997
In his paper, we introduce a novel technique, called F-APACS, for mining jkzy association rules. &istlng algorithms involve discretizing the domains of quantitative attrilmtes into intervals so as to discover quantitative association rules. i%ese intervals may not be concise and meaning@ enough for human experts to easily obtain nontrivial knowledge ...
Keith C. C. Chan, Wai-Ho Au
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Mining negative association rules

Proceedings ISCC 2002 Seventh International Symposium on Computers and Communications, 2003
The focus of this paper is the discovery of negative association rules. Such association rules are complementary to the sorts of association rules most often encountered in the literature and have the forms of X/spl rarr/ -Y or -X/spl rarr/Y. We present a rule discovery algorithm that finds a useful subset of valid negative rules.
null Xiaohui Yuan, B.P. Buckles, null Zhaoshan Yuan, null Jian Zhang   +3 more
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Mining weighted association rules

Intelligent Data Analysis, 2001
Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity.
Lu, Songfeng, Hu, Heping, Li, Fan
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Mining Utility Association Rules

Proceedings of the 2018 10th International Conference on Computer and Automation Engineering, 2018
Mining high utility itemset is to find the itemsets that can bring higher profits to the company, which considers both of the profits and purchased quantities for the items. However, from the high utility itemsets, we cannot know what products should be recommended to the customer such that the profit can be increased when he/she bought some products ...
Yue-Shi Lee, Show-Jane Yen
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Association Rule Mining I

2013
This chapter looks at the problem of finding any rules of interest that can be derived from a given dataset, not just classification rules as before. This is known as Association Rule Mining or Generalised Rule Induction. A number of measures of rule interestingness are defined and criteria for choosing between measures are discussed.
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Mining generalized association rules

Future Generation Computer Systems, 1997
Abstract We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy.
Ramakrishnan Srikant, Rakesh Agrawal
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Mining Association Rules

2009
During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules.
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