Download Advanced Data Mining and Applications: 7th International by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin PDF

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 court cases of the seventh overseas convention on complex info Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers provided including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers conceal quite a lot of subject matters providing unique learn findings in info mining, spanning functions, algorithms, software program and structures, and utilized disciplines.

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Additional info for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I

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The MUSHROOM dataset contains characteristics from different species of mushrooms. The data characteristics are summarized in Tab. 1. The estMax algorithm in [29] is a state-of-the-art method for mining maximal frequent itemsets over stream; thus, we use it as the evaluated method for comparison. 01; also, we employ our presented naive algorithm as another evaluated method. 1, which denotes a 10 percent probability for mistaken deleting the actual maximal frequent itemsets. A False Negative Maximal Frequent Itemset Mining Algorithm over Stream 37 Table 1.

VLDB (1994) 4. : Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery 7, 5–22 (2003) 5. : Mining Frequent Itemsets in a Stream. In: Proc. ICDM (2007) 6. : Mining All Non-Derivable Frequent Itemsets. , Toivonen, H. ) PKDD 2002. LNCS (LNAI), vol. 2431, p. 74. Springer, Heidelberg (2002) 7. : Decaying Obsolete Information in Finding Recent Frequent Itemsets over Data Stream. IEICE Transaction on Information and Systems 87(6), 1588–1592 (2004) 8.

Definition 2(Actual Un-Maximal Frequent Itemset). If an itemset X is an actual frequent itemset, and it is covered by any other actual frequent itemsets, it is called an actual un-maximal frequent itemset(AUMF ). Definition 3(Actual Inter-Maximal Frequent Itemset). If an itemset X is an actual frequent itemset and covered by shifty frequent itemsets, it is called an actual inter-maximal frequent itemset(AIMF ). AIMF AUMF AF SF PF AMF AF SF PF∪IF∪Φ UMF UMF MF UMF MF ⊗ UMF ⊗ ⊗ Fig. 1. Covering Relationship SUMF SMF PMF 34 H.

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