Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Classification with Degree of Membership: A Fuzzy Approach
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fuzzy Clustering Based on Modified Distance Measures
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity-driven cluster merging method for unsupervised fuzzy clustering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework
New Frontiers in Applied Data Mining
A fuzzy threshold based modified clustering algorithm for natural data exploration
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Fuzzy versus quantitative association rules: a fair data-driven comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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In Association rule mining, the quantitative attribute values are converted into Boolean values using fixed intervals. Conventional association rule mining algorithms are then applied to find relations among the attribute values. These intervals may not be concise and meaningful enough for human users to easily obtain non trivial knowledge from those rules discovered. Clustering techniques can be used for segmenting quantitative values into meaningful groups instead of fixed intervals. But the conventional clustering techniques like k-means and c-means require the user to specify the number of clusters and initial cluster centres. This initialization is one of the major challenges of clustering. A novel fuzzy based unsupervised clustering algorithm proposed by the authors is extended to segment quantitative values into fuzzy clusters in this paper. Membership values of quantitative items in the partitioning fuzzy clusters are used with weighted fuzzy rule mining techniques to find natural association rules. This fuzzy based method for handling quantitative attributes is compared with that of fixed intervals and segmenting using conventional k-means clustering method along with Apriori algorithm.