Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Fuzzy c-Means Clustering for Large Data Sets
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Association mining of dependency between time series using Genetic Algorithm and discretisation
International Journal of Business Intelligence and Data Mining
Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generation
International Journal of Business Intelligence and Data Mining
Privacy preservation for associative classification: an approximation algorithm
International Journal of Business Intelligence and Data Mining
Redundant association rules reduction techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Fuzzy versus quantitative association rules: a fair data-driven comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
In Defense of Fuzzy Association Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Finding Associations in Composite Data Sets: The CFARM Algorithm
International Journal of Data Warehousing and Mining
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
Association rule mining ARM algorithms work only with binary attributes, and expect quantitative attributes to be converted to binary ones using sharp partitions, like 'age = [25, 60]'. A better alternative is to convert quantitative attributes to fuzzy attributes, like 'age = middle-aged', to eliminate loss of information due to sharp partitioning, and then run a fuzzy ARM algorithm. The most popular fuzzy ARM algorithms are fuzzy adaptations of apriori. Fuzzy apriori, like apriori, is a slow algorithm, especially for most medium-sized 500 K to 1 M and large > 1 M datasets. We propose a new fuzzy ARM algorithm called FAR-miner for fast and efficient performance. Through experiments we show that FAR-miner is 8-19 and 6-10 times faster on large and medium-sized datasets respectively as compared to fuzzy apriori. This efficiency is due to properties like two-phased multiple-partition tidlist-style processing and byte-vector representation and effective compression of tidlists.