S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, DaWaK 2001. Home/Search Document Details and Download
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A Scalable Multi-Strategy Algorithm for Counting.. - Orlando, Palmerini.. (2002) Self-citation (Orlando Palmerini Perego) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, DaWaK 2001.
Adaptive and Resource-Aware Mining of Frequent Sets - Orlando Palmerini Perego (2002) (7 citations) Self-citation (Orlando Palmerini Perego) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, DaWaK 2001.
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets - Claudio Lucchese Salvatore Self-citation (Orlando Palmerini Perego) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of 3rd Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK 01) - Munich, Germany, volume 2114 of LNCS, pages 71--82. Springer, 2001.
Scheduling High Performance Data Mining Tasks on a .. - Orlando.. Self-citation (Orlando Palmerini Perego) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of 3rd Int. Conf. DaWaK 01 - Munich, Germany. LNCS Spinger-Verlag, 2001.
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets - Claudio Lucchese Salvatore Self-citation (Orlando Palmerini Perego) (Correct)
No context found.
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of 3rd Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK 01) - Munich, Germany, volume 2114 of LNCS, pages 71--82. Springer, 2001.
A Scalable Multi-Strategy Algorithm for Counting.. - Orlando, Palmerini.. (2002) Self-citation (Orlando Palmerini Perego) (Correct)
No context found.
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, DaWaK 2001.
Scheduling High Performance Data Mining Tasks on a .. - Orlando.. Self-citation (Orlando Palmerini Perego) (Correct)
No context found.
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of 3rd Int. Conf. DaWaK 01 - Munich, Germany. LNCS Spinger-Verlag, 2001.
Adaptive and Resource-Aware Mining of Frequent Sets - Orlando Palmerini Perego (2002) (7 citations) Self-citation (Orlando Palmerini Perego) (Correct)
No context found.
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, DaWaK 2001.
Scheduling High Performance Data Mining Tasks on a .. - Orlando.. (2002) Self-citation (Orlando Palmerini Perego) (Correct)
....is when F is a linear function of the sample rate s = D and we can therefore write e i (1 s)# where # is the slope of the curve. We analyzed two algorithms: DCP, a fully optimized DM algorithm for Frequent Set Counting which exploits out of core techniques to enhance scalability [8], and k means [1] the popular clustering algorithm. We ran DCP and k means on synthetic datasets by varying the size of the sample considered. The results of the experiments are promising: both DCP and k means exhibits quasi linear scalability with respect to the size of the sample when the user ....
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of 3rd Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK 01) - Munich, Germany. LNCS Spinger-Verlag, 2001.
An Efficient Parallel and Distributed Algorithm.. - Orlando.. (2002) Self-citation (Orlando Palmerini Perego) (Correct)
....initial counting based phase, ParDCI exploits a horizontal database with variable length records. During this phase, ParDCI trims the transaction database as execution progresses. In particular, a pruned dataset k 1 is written to the disk at each iteration k, and employed at the next iteration [11]. Dataset pruning is based on several criteria. The main criterium states that transactions that do not contain any frequent k itemset will not surely contain larger frequent itemsets and can thus be removed from k 1 . Pruning entails a reduction in I O activity as the algorithm progresses, ....
....of ParDCI. Since the counting based approach is used only for few iterations (in all the experiments conducted ParDCI starts using intersections at the third or fourth iteration) in the following we only sketch the main features of the counting method adopted (interested readers can refer to [11]) In the first iteration, as all FSC algorithms, ParDCI directly counts the occurrences of items within all the transactions. For k 2, instead of using complex data structures like hash trees or prefix trees, ParDCI uses a novel Direct Count technique that can be thought as a generalization of ....
S. Orlando, P. Palmerini, and R. Perego. Enhancing the Apriori Algorithm for Frequent Set Counting. In Proc. of the 3 rd Int. Conf. on Data Warehousing and Knowledge Discovery, LNCS 2114, pages 71--82, Germany, 2001.
Survey on Frequent Pattern Mining - Goethals (2003) (4 citations) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the apriori algorithm for frequent set counting. In Y. Kambayashi, W. Winiwarter, and M. Arikawa, editors, Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, volume 2114 of Lecture Notes in Computer Science, pages 71--82. Springer, 2001.
Efficient Frequent Pattern Mining - Goethals (2002) (Correct)
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S. Orlando, P. Palmerini, and R. Perego. Enhancing the apriori algorithm for frequent set counting. In Y. Kambayashi, W. Winiwarter, and M. Arikawa, editors, Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, volume 2114 of Lecture Notes in Computer Science, pages 71--82. Springer, 2001.
Incremental Mining of Frequent Patterns Without Candidate.. - Cheung, Zaiane (Correct)
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Orlando, S., Palmerini, P., and Perego, R. Enhancing the Apriori Algorithm for Frequent Set Counting. Proceedings of 3rd International Conference on Data Warehousing and Knowledge Discovery. 2001.
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