Proceedings Article10.1109/ICSMC.2006.385264
A Quantitative Association Rule Mining Algorithm Based on Clustering Algorithm
Toshihiko Watanabe,Hirokazu Takahashi +1 more
- 01 Oct 2006
- Vol. 3, pp 2652-2657
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TL;DR: From the results of numerical experiments using benchmark data, the method of quantitative association rule extraction that can quantize the attribute by applying clustering algorithm and extract rules simultaneously is found to be promised for actual applications.
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Abstract: In order to develop a data mining system for huge database mainly composed of numerical attributes, there exists necessary process to decide valid quantization of the numerical attributes. Though the clustering algorithm can provide useful information for the quantization problem, it is difficult to formulate appropriate clusters for rule extraction in terms of cluster size and shape. In this paper, we propose a new method of quantitative association rule extraction that can quantize the attribute by applying clustering algorithm and extract rules simultaneously. From the results of numerical experiments using benchmark data, the method is found to be promised for actual applications.
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Citations
An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules
TL;DR: A basic algorithm based on the Apriori algorithm for rule extraction utilizing redundancy of the extracted rules to improve the computational time of mining for the actual application.
7
Fuzzy Clustering-Based Quantitative Association Rules Mining in Multidimensional Data Set
Jining Jia,Yongzai Lu,Jian Chu,Hongye Su +3 more
- 25 Jun 2015
TL;DR: The simulation experiment results show that the proposed Fuzzy Pattern Fusion based on Competitive Agglomeration (FPF-CA) algorithm can efficiently mine quantitative association rules according to the actual data distribution.
2
Fuzzy Association Rules Extraction Based on FCV Algorithm
Toshihiko Watanabe,Hirokazu Takahashi +1 more
- 01 Jan 2006
TL;DR: F fuzzy association rules extraction method that can quantize the attributes by applying FCV clustering algorithm and extract rules simultaneously is found to be promising for actual applications.
Value of Fuzzy Logic for Data Mining and Machine Learning: A Case Study
TL;DR: A novel speed-up technique is proposed in this paper to support association rule mining (ARM), which is a clustering-based one and provides fusion of clustering and ARM.
References
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Ramakrishnan Srikant,Rakesh Agrawal +1 more
- 01 Jun 1996
TL;DR: This work deals with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary and introduces measures of partial completeness which quantify the information lost due to partitioning.
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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.
Mining quantitative association rules in large relational tables
TL;DR: The problem of mining association rules in large relational tables containing both quantitative and categorical attributes is introduced and an example of such an association might be "10% of married p...
584
Fuzzy association rules and the extended mining algorithms
Guoqing Chen,Qiang Wei +1 more
TL;DR: The paper deals with the fuzziness based upon fuzzy taxonomies that reflect partial belongings among itemsets, as well as upon the extended settings for the degree of support and the level of confidence.
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