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Abstract: This paper describes how to increase the efficiency of inductive data mining algorithms by replacing the central matching operation with a marker propagation technique. Breadth-first marker propagation is most beneficial when the data are linked to hierarchical background knowledge (e.g., tree-structured attributes), or when the attributes describing the data have many values. We support our claims analytically with complexity arguments and empirically on several large data sets. We... (Update)
Context of citations to this paper: More
...ST0 and ST1. The results in Table 4 indicate that IND s classification accuracy is not adversely affected by such heuristics; see Aronis and Provost (1997) for another possible heuristic. Since T1 is a one level tree, it may appear surprising that it is not faster than...
...results in Table 4 suggest that IND s classification accuracy is not adversely affected by such heuristics. The technique in Aronis and Provost (1997) may also be useful in increasing the efficieny of some algorithms when there are categorical variables with many categories....
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BibTeX entry: (Update)
Aronis, J. and F. Provost (1997). Increasing the efficiency of data mining algorithms with breadthfirst marker propagation. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA. http://citeseer.ist.psu.edu/aronis97increasing.html More
@inproceedings{ aronis97increasing,
author = "John M. Aronis and Foster J. Provost",
title = "Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation",
booktitle = "Knowledge Discovery and Data Mining",
pages = "119-122",
year = "1997",
url = "citeseer.ist.psu.edu/aronis97increasing.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
23 Scaling up inductive learning with massive parallelism - Provost, Aronis - 1996 ACM DBLP
15 Combining data mining and machine learning for effective use.. - Fawcett, Provost - 1996 DBLP
13 Exploiting background knowledge in automated discovery - Aronis, Provost et al. - 1996
13 Linear-Time Rule Induction - Domingos - 1996 DBLP
12 The use of background knowledge in decision tree induction (context) - nez - 1991 ACM DBLP
5 On handling tree-structured attributes in decision tree lear.. (context) - Almuallim, Akiba et al. - 1995
1 Presented at 1995 North American Congress of Clinical Toxico.. (context) - datura, poisoning et al. - 1995
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