Agents and Data Mining Interaction: 7th International by Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas

By Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)

This publication constitutes the completely refereed post-workshop court cases of the seventh foreign Workshop on brokers and knowledge Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may possibly 2011 along side AAMAS 2011, the tenth overseas Joint convention on self sustaining brokers and Multiagent platforms.
The eleven revised complete papers provided have been rigorously reviewed and chosen from 24 submissions. The papers are equipped in topical sections on brokers for info mining; facts mining for brokers; and agent mining applications.

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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

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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. In: Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data, WA, pp. 207–216 (May 1993) 11. : Fast algorithms for mining association rules. In: Proc. 1994 Int. Conf. Very Large Data Bases, Santiago, Chile, pp. 487–499 (September 1994) 12. : Parallel Algorithm for Mining Frequent Itemsets. In: Proc. of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, August 18-21 (2005) 13.

Table 4. Comparison of the result accuracy provided by K-means task distribution before and after cluster configuration improvement No. 93 Evaluation To evaluate our approach we experimented with a selection of data sets taken from the UCI machine learning repository [14]. We compared the operation of our MADM approach with the well known K-means [22] and KNN [15] clustering algorithms. In each case we recorded the accuracy of the clustering operation, with respect to the known (ground truth) clustering.

Support of the rule is greater than or equal to c(P ⇒ Q ) = p(Q P ) = 2. , confidence of the rule is greater than or equal to the given minimum threshold confidence Association Rule Mining(ARM) ARM is the task to find all the strong association rules from the frequent itemsets. The ARM can be viewed as a two-step process[12]. 1. Find all frequent k-itemsets( Lk ) 2. Generate Strong Association Rules from a) For each frequent itemset Lk l ∈ Lk , generate all non empty subsets of l . 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.

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