Kuramochi, M., Karypis, G.: An Efficient Algorithm for Discovering Frequent Subgraphs, Technical Report, University of Minesota, 2002. Home/Search Document Details and Download
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Mining the Space of Graph Properties - Jeh, Widom (Correct)
..... 1#j#i 0.9 1#j (i.e. when at least 90 of the pseudopredicates have been accounted for) We have found this heuristic to work well in most cases, providing the results illustrated in the previous figures. 6 Related Work Much of the existing work on mining graph data, e.g. [7, 12, 16], extends the traditional data mining problem of finding frequent itemsets in market basket data [3] The focus in graphs is on finding frequent substructures, the graph equivalent of frequent itemsets. The focus of our work is on finding interesting properties in graphs, of which a substructure ....
Michihiro Kuramochi and George Karypis. An efficient algorithm for discovering frequent subgraphs. Technical report, Department of Computer Science, University of Minnesota, 2002. http://www.cs.umn.edu/kuram/ papers/fsg-long.pdf.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2004) (2 citations) Self-citation (Kuramochi Karypis) (Correct)
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M. Kuramochi and G. Karypis. An efficient algorithm for discovering frequent subgraphs. Technical Report 02-026, University of Minnesota, Department of Computer Science, 2002.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2004) (2 citations) Self-citation (Kuramochi Karypis) (Correct)
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M. Kuramochi and G. Karypis. An efficient algorithm for discovering frequent subgraphs. IEEE Transactions on Knowledge and Data Engineering. in press.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2003) (2 citations) Self-citation (Kuramochi Karypis) (Correct)
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M. Kuramochi and G. Karypis. An efficient algorithm for discovering frequent subgraphs. Technical Report 02-026, University of Minnesota, Department of Computer Science, 2002.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2003) (2 citations) Self-citation (Kuramochi Karypis) (Correct)
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M. Kuramochi and G. Karypis. An efficient algorithm for discovering frequent subgraphs. IEEE Transactions on Knowledge and Data Engineering. in press.
Frequent Sub-Structure-Based Approaches for.. - Deshpande, Kuramochi, .. (2003) (4 citations) Self-citation (Kuramochi Karypis) (Correct)
.... called quantitative structure activity relationships (QSAR) 15, 16, 1] whereas the second class operates directly on the structure of the chemical compound and try to automatically identify a small number of chemical sub structures that can be used to discriminate between the different classes [3, 43, 18, 25]. A number of comparative studies [40, 20] have shown that techniques based on the automatic discovery of chemical sub structures are superior to those based on QSAR properties and require limited user intervention and domain knowledge. However, despite their success, a key limitation of these ....
....them. The advantage of such an approach is that during classification model construction, all relevant sub structures are available allowing the classifier to intelligently select the most discriminating ones. To ensure that such an approach is computationally scalable, we use recently developed [23, 25] highly efficient frequent subgraph discovery algorithms coupled with aggressive feature selection to reduce both the amount of time required to build as well as to apply the classification model. In addition, we present a sub structure discovery algorithm that finds a set of sub structures whose ....
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Michihiro Kuramochi and George Karypis. An efficient algorithm for discovering frequent subgraphs. Technical Report TR# 02-26, Dept. of Computer Science and Engineering, University of Minnesota, 2002.
Frequent Subtree Mining - An Overview - Chi, Nijssen, al. (2001) (1 citation) (Correct)
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Kuramochi, M., Karypis, G.: An Efficient Algorithm for Discovering Frequent Subgraphs, Technical Report, University of Minesota, 2002.
Mining Relaxed Graph Properties in Internet - Hämäläinen, Toivonen, al. (Correct)
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M. Kuramochi and G. Karypis. An efficient algorithm for discovering frequent subgraphs. Technical Report 02-026, Department of Computer Science, University of Minnesota, 2002.
Parallel Mining for Frequent Fragments on a.. - Meinl, Fischer.. (2005) (Correct)
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Michihiro Kuramochi and George Karypis. An efficient algorithm for discovering frequent subgraphs. IEEE Trans. on Knowledge and Data Engineering, 16(9):1038--1051, September 2004.
Frequent Subtree Mining - An Overview - Chi, Nijssen, Muntz, Kok (2005) (1 citation) (Correct)
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Kuramochi, M., Karypis, G.: An Efficient Algorithm for Discovering Frequent Subgraphs, Technical Report, University of Minesota, 2002.
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