By Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)
AI 2001 is the 14th within the sequence of Arti cial Intelligence meetings subsidized by means of the Canadian Society for Computational stories of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As used to be the case final 12 months too, the convention is being held along with the once a year meetings of 2 different Canadian societies, photos Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the final event might be enriched via this conjunction of meetings. This 12 months is the \silver anniversary" of the convention: the rst Canadian AI convention was once held in 1976 at UBC. in the course of its lifetime, it has attracted Canadian and overseas papers of top quality from quite a few AI study components. All papers submitted to the convention acquired at the least 3 indep- dent stories. nearly one 3rd have been authorized for plenary presentation on the convention. the simplest paper of the convention should be invited to seem in Computational Intelligence.
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Extra info for Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings
However, data stored in the database are often updated making association rules discovered in the previous database possibly no longer interesting rules in the new database. Furthermore, some new interesting rules may have developed in the new database which were not in the old database. Thus, work on incremental maintenance of association rules include [3,2]. In this paper, we present a new algorithm that efficiently generates incremental association rules in the updated database by applying the Apriori property .
Iii) as the size of support increases, the difference in execution times of MAAP algorithm and FUP2 diminishes. This is because as the support increases, the total number of large itemsets and candidate itemsets decreases reducing the need to spend time searching huge database for support of large itemsets. – Experiment 2: Given a fixed size dataset (including inserted and deleted datasets) and a fixed support, we test CPU execution times when different numbers of old large itemsets are allowed to change in the new database.
Pantel and D. Lin Term Effe ct on Pe rplexity 665 660 655 650 645 1 41 81 121 161 201 241 281 Ranke d Te rms by our Extractor Fig. 4. The effect on perplexity when each ranked term is added individually. The left-most points represent the highest ranked terms. H ( p , m) = - 1 log 2 m(t ) n t˛ L (6) where t is a term or a word in L. e. the lower H(p, m) will be). Hence, this model can serve to perform a comparative evaluation of different term extraction algorithms. 1 Analysis We used two corpora for evaluating the perplexity of our system: UNTS  consisting of 439,053 words and NAG  consisting of 117,582 words.