T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In SDM, 2002. Home/Search Document Details and Download
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gSpan: Graph-Based Substructure Pattern Mining - Yan, Han (2002) (33 citations) (Correct)
....are children of 2(b) and 2(e.0) e.2) are children of 2(e) Backward edges can only grow from the rightmost vertex while forward edges can grow from vertices on the rightmost path. This restriction is similar to TreeMinerV s equivalence class extension [8] and FREQT s rightmost expansion [2] in frequent tree discovery. The enumeration order of these children is enhanced by the DFS lexicographic order, i.e. it should be in the order of 2(b) 2(c) 2(d) 2(e) and 2(f) Definition 4 (DFS Code Tree) In a DFS Code Tree, each node represents a DFS code, the relation between parent and ....
T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa. Efficient substructure discovery from large semistructured data. In SIAM SDM'02, April 2002.
Online Algorithms for Mining Semi-structured Data Stream - Tatsuya Asai Hiroki (2002) (2 citations) Self-citation (Asai Abe Kawasoe Arimura Arikawa) (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. Efficient Substructure Discovery from Large Semistructured Data. In Proc. the 2nd SIAM Int'l Conf. on Data Mining (SDM2002.
Online Algorithms for Mining Semi-structured Data Stream - Asai, al. (2002) (2 citations) Self-citation (Asai Abe Kawasoe Arimura Arikawa) (Correct)
....with bounded working space. As another idea, we adopt a candidate management policy similar to Hidher [11] for online association mining to limit the number of candidate patterns as small as possible. We also use the enumeration technique for labeled ordered trees that we recently proposed in [4], a generalization of a technique by Bayardo [6] Combining these ideas, our algorithm StreamT works efficiently in both time and space complexities in online manner. Furthermore, we extend our algorithm to the forgetting model of online data stream mining, where the effect of a past data item ....
....6, we conclude. For proofs not found here, see [5] 1. 1 Related Works Emerging technologies of semi structured data have attracted wide atten tion of networks, e commerce, information retrieval and databases [2, 19] In contrast, there have not been many studies on semi structured data min ing [1, 4, 7, 9, 13, 15, 16, 20, 22]. There are a body of researches on online data processing and mining [10, 14, 18] Most related work is Hidher [11] who proposed a model of continuous pattern discovery from unbounded data stream, and presented adaptive online algorithm for mining association rules. Parthasarathy et al. 17] and ....
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. Efficient Substructure Discovery from Large Semistructured Data. In Proc. the 2nd SIAM Int'l Conf. on Data Mining (SDM2002.
Fast On-line Kernel Learning for Trees - Fabio Aiolli Giovanni (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In SDM, 2002.
Frequent Subtree Mining - An Overview - Chi, Nijssen, al. (2001) (1 citation) (Correct)
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Asai, T., Abe, K., Kawasoe, S., Arimura, H., Satamoto, H., Arikawa, S.: Efficient Substructure Discovery from Large Semi-Structured Data, 2nd SIAM Int. Conf. on Data Mining, April 2002.
Mining Closed and Maximal Frequent Subtrees from Databases of .. - Yun Chi Student (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa, "Efficient Substructure Discovery from Large SemiStructured Data," Proc. Second SIAM Int'l Conf. Data Mining, Apr. 2002.
HybridTreeMiner: An Efficient Algorithm for Mining Frequent.. - Chi, Yang, Muntz (2004) (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In 2nd SIAM Int. Conf. on Data Mining (SDM'02), 2002.
Fast Mining of Frequent Tree Structures By Hashing and.. - Dimitrios Katsaros.. (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, S. Arikawa, Efficient substructure discovery from large semi-structured data, in: Proceedings of the Second SIAM Conference on Data Mining (SDM), 2002, pp. 158--174.
Efficiently Mining Frequent Embedded Unordered Trees - Zaki (2005) (Correct)
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Asai, T., Abe, K., Kawasoe, S., Arimura, H., Satamoto, H., Arikawa, S.: Efficient Substructure Discovery from Large Semi-structured Data, 2nd SIAM Int'l Conference on Data Mining, April 2002.
Efficiently Mining Frequent Trees in a Forest: Algorithms and.. - Zaki (2005) (Correct)
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T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa, "Efficient Substructure Discovery from Large SemiStructured Data," Proc. Second SIAM Int'l Conf. Data Mining, Apr. 2002.
Frequent Subtree Mining - An Overview - Chi, Nijssen, Muntz, Kok (2005) (1 citation) (Correct)
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Asai, T., Abe, K., Kawasoe, S., Arimura, H., Satamoto, H., Arikawa, S.: Efficient Substructure Discovery from Large Semi-Structured Data, 2nd SIAM Int. Conf. on Data Mining, April 2002.
Mining Closed and Maximal Frequent Subtrees from Databases .. - Chi, Xia, Yang, Muntz (Correct)
No context found.
T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In 2nd SIAM Int. Conf. on Data Mining, April 2002.
HybridTreeMiner: An Efficient Algorithm for Mining Frequent.. - Chi, Yang, Muntz (2004) (Correct)
No context found.
T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In 2nd SIAM Int. Conf. on Data Mining (SDM'02), 2002.
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