By Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed complaints of the seventeenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the full of ninety eight papers provided in those lawsuits was once rigorously reviewed and chosen from 363 submissions. They disguise the final fields of information mining and KDD widely, together with development mining, category, graph mining, functions, computer studying, characteristic choice and dimensionality relief, a number of info assets mining, social networks, clustering, textual content mining, textual content type, imbalanced facts, privacy-preserving information mining, advice, multimedia facts mining, circulation information mining, facts preprocessing and representation.
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Additional info for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I
Mining attributed subgraphs independently of labels of vertices is impossible with this approach. Several studies [10,15,16] deal with attributed graphs but are looking for frequent subgraphs sharing common sets of attributes. Our work diﬀers from these studies in the sense that itemsets associated with the vertices of a given frequent substructures are not necessarily identical. 4 Mining Frequent Atrees We are mainly interested in identifying induced ordered and unordered asubtrees. Depending on applications, some patterns including gaps in the ancestordescendant relationship can also be considered.
However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent pattern mining from uncertain data. When handling uncertain data, UF-growth and UFP-growth are examples of well-known mining algorithms, which use the UF-tree and the UFP-tree respectively. However, these trees can be large, and thus degrade the mining performance. In this paper, we propose (i) a more compact tree structure to capture uncertain data and (ii) an algorithm for mining all frequent patterns from the tree.
M. ) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997) PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data Carson Kai-Sang Leung and Syed Khairuzzaman Tanbeer Dept. ca Abstract. Many existing algorithms mine frequent patterns from traditional databases of precise data. However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent pattern mining from uncertain data. When handling uncertain data, UF-growth and UFP-growth are examples of well-known mining algorithms, which use the UF-tree and the UFP-tree respectively.