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John L. Pfaltz and Christopher M. Taylor. Concept Lattices as a Scientific Knowledge Discovery Technique. In 2nd SIAM International Conference on Data Mining, pages 65--74, Arlington, VA, Apr. 2002.

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Closed Set Mining of Biological Data - John Pfaltz Univ (2002)   (4 citations)  Self-citation (Pfaltz Taylor)   (Correct)

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John L. Pfaltz and Christopher M. Taylor. Concept Lattices as a Scientific Knowledge Discovery Technique. In 2nd SIAM International Conference on Data Mining, pages 65--74, Arlington, VA, Apr. 2002.


Current State of Data Mining - Drewry, Gu, Hocking, Kang, Schutt.. (2002)   Self-citation (Pfaltz Taylor)   (Correct)

....rules for the mushroom database described in section 9.2, which is generally considered dense, when min sup = 0:01 and min conf = 0:5. Given min sup = 0:2 and min conf = 0:9, it generates over 340,000 rules. Furthermore, it may not capture an important but infrequent rule. It was shown in [27] that apriori algorithm misses some rules detecting poisonous mushrooms. See Section 9.2. In summary, probabilistic rule mining may not represent capture causal relations between antecedents and consequents. This limits the applicability of probabilistic rule mining approaches to some data sets, ....

....and similar algorithms, must make more than one sweep over R, it must be fixed. But, as Godin and Missaoui have shown, incremental creation and update of concept lattices is much faster [13, 14] The problem is that their approach to incremental update does not update the generators as well. In [27] Theorem 6.1 is used to determine the generators of each closed set based on the faces of (sets covered by) the set. A major problem is that as more data is read, attribute combinations that originally passed the min sup test may fail in the expanded data set and vice versa. This creates ....

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John L. Pfaltz and Christopher M. Taylor. Concept Lattices as a Scientific Knowledge Discovery Technique. In 2nd SIAM International Conference on Data Mining, pages 65--74, Arlington, VA, Apr. 2002.

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