Proceedings Article10.1109/ICMLC.2002.1175424
Variable precision rough set model based dataset partition and association rule mining
Quan-De Wang,Xian-Jia Wang,Xian-Pei Wang +2 more
- 04 Nov 2002
- Vol. 4, pp 2175-2179
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TL;DR: A method of dataset-partitioning using conceptual hierarchy and a variable precision rough set model is presented and an algorithm for mining association rules using this technique is designed, and an asynchronous algorithm is proposed, too.
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Abstract: Discovery of association rules is one of the most important tasks in data mining. Many efficient algorithms have been proposed in the literature. In this paper, a method of dataset-partitioning using conceptual hierarchy and a variable precision rough set model is presented. An algorithm for mining association rules using this technique is designed, and an asynchronous algorithm is proposed, too. The efficiency of the algorithm and the factors that affect the efficiency of the algorithm are analyzed by mining association rules in a dataset artificially generated. The result of an experiment proves the efficiency of the algorithm.
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Citations
Mining Pinyin-to-character conversion rules from large-scale corpus: a rough set approach
Wang Xiaolong,Chen Qingcai,D.S. Yeung +2 more
- 01 Apr 2004
TL;DR: Results show that by the smoothing method, high precision conversion and recall rates can be achieved even for rules represented directly by pinyin rather than words, and a comparison with the baseline tri-gram model also shows good complement between the method and the tri- gram language model.
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An Improved Economic Early Warning Based on Rough Set and Support Vector Machine
Xiu-Li Pang,Yuqiang Feng +1 more
- 01 Aug 2006
TL;DR: A new method of combining rough sets and support vector machine, where rough set is applied to overcome the noise problem and eliminate the redundant economic information; and support vectors based on structural risk minimization principle is used to solve the over-fitting and small-scale sample problem is proposed.
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The Categorical Distinction Annexations to Maximal Associations Discovered from Web Database by Rough Set Theory to Increase the Quality
Erkan Ülker,Eyüp Siramkaya,Ahmet Arslan +2 more
- 01 Jan 2008
TL;DR: The oldest and most widely accepted algorithm used in the exploration (discovery) of association rules is the Apriori algorithm, the basic algorithm to determine the frequency of item sets, proposed by R. Agrawal et al. in 1993.
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Fast algorithms for mining association rules
Rakesh Agrawal,Ramakrishnan Srikant +1 more
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TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
- 16 May 2000
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
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Mining Generalized Association Rules
Ramakrishnan Srikant,Rakesh Agrawal +1 more
- 11 Sep 1995
TL;DR: In this paper, the problem of mining generalized association rules was introduced, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, finding associations between items at any level of the taxonomy.
•Proceedings Article
Discovery of Multiple-Level Association Rules from Large Databases
Jiawei Han,Yongjian Fu +1 more
- 11 Sep 1995
TL;DR: A top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques.
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•Proceedings Article
Rough Set Theory under the Similarity Relations
Keyun Hu,Yuefei Sui,Ju Wang,Yuchang Lu +3 more
- 22 Jul 2001
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