By Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)
This booklet constitutes the completely refereed post-workshop complaints of the seventh overseas Workshop on brokers and knowledge Mining interplay, ADMI 2011, held in Taipei, Taiwan, in might 2011 together with AAMAS 2011, the tenth overseas Joint convention on independent brokers and Multiagent platforms.
The eleven revised complete papers provided have been rigorously reviewed and chosen from 24 submissions. The papers are geared up in topical sections on brokers for info mining; info mining for brokers; and agent mining applications.
Read or Download Agents and Data Mining Interaction: 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers PDF
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Extra resources for Agents and Data Mining Interaction: 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers
Agent Enriched Distributed Association Rules Mining: A Review b) For every non empty subset 35 s of l , output the rule “ s ⇒ (l − s ) ” if sup_ count (l ) ≥ min_ conf , where min_conf is minimum threshold sup_ count ( s ) confidence. 5 Distributed Association Rule Mining (DARM) Distributed Association Rule Mining (DARM) task is to find all the strong association rules in a distributed environment. The DARM can also be viewed as a two-step process. GFI 1. Find the Global frequent k-itemset ( Lk k-Itemsets LFI k (i ) L ) from the distributed Local Frequent from partitioned datasets.
AAAI/MIT Press (2000) 7. : Distributed Data Mining: An Overview. In: Newsletter of the IEEE Technical Committee on Distributed Processing, pp. 5–9 (Spring 2001) 8. : Parallel and Distributed Methods for Incremental Frequent Itemset Mining. IEEE Transactions on Systems, Man, and Sybernetics- Part B: Cybernetics 34(6) (December 2004) 9. : Mining Frequent Patterns without candidate generation. In: Proc. ACM-SIGMOD, Dallas, TX (May 2000) 10. : Mining association rules between sets of items in large databases.
Dencrypt Secure Union Agent (DSUA):The DSUA travels through every host to pursue decryption. c. Encrypt Sum Agent (ESA):ESA carries Rule Set containing pairs of item_label, count. It travels through all the hosts to obtain the encrypted support count. d. Decrypt Sum Agent (DSA): DSA carries the array of RuleSet extracted from the returned ESA. It travels through all the hosts and let each host subtract the random number they generate when dealing with the ESA. e. Broadcast Agent (BA): When the DSA agent comes back to agent master the globally frequent k-itemsets ( Fk ) can be calculated from the decrypted RuleSet then BA is used to carry Fk to each host to update their knowledge.