By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed lawsuits of the seventh overseas convention on complex information Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers offered including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers conceal a variety of subject matters proposing unique learn findings in information mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
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Additional resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I
Also the last attribute, Citations (C), is well deﬁning for each cluster. As we wish that the clustering result would contain clusters of objects that are all similar to each other and dissimilar from the rest, we could model this by a similarity relation that contains +1 values between objects inside the same cluster and −1 values between objects in diﬀerent clusters. To measure the quality of the results we use the ﬁtness of a partition that we deﬁned in Section 3. We compute this ﬁtness accroding to the similarity relation deﬁned by each point of view that we have modeled.
4. Precision of maximal frequent itemsets VS. 03 Mimimum Support (d) MUSHROOM Fig. 5. Recall of maximal frequent itemsets VS. Minimum support 39 40 H. Li and N. Zhang to that of the naive method and estMax with a little lower, that is to say, our algorithm mistaken deletes little real results. 6 Conclusions In this paper we considered a problem, which is how to mine maximal frequent itemset over stream using a false negative method, and then proposed our method FNMFIMoDS. In our algorithm, we used Chernoﬀ Bound to prune the infrequent itemsets; plus, we classiﬁed the itemsets into categories to prune the un-maximal frequent itemsets, which still can guarantee that we obtain the proper itemsets; thus, our algorithm was able to perform in an incremental manner.
Bruneman, P. ) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998) 26. : On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery. IEEE Transactions on Knowledge and Data Engineering 18(2), 202–216 (2006) 27. : A Regression-Based Temporal Pattern Mining Scheme for Data Streams. In: Proc. VLDB (2003) 28. : Weighted Association Rule Mining using Weighted Support and Signiﬁcance Framework. In: Proc. SIGKDD (2003) 29. : estMax: Tracing Maximal Frequent Itemsets over Online Data Streams.
Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)