2012
DOI: 10.1016/j.knosys.2011.01.013
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Mining interestingness measures for string pattern mining

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Cited by 10 publications
(5 citation statements)
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“…Vo and Le (2011) employed lattice and hash tables to mine ARs using a correlated measure from large databases. Baena-García and Morales-Bueno proposed an algorithm for mining frequent sequences with interestingness measures (Baena-García & Morales-Bueno, 2012). Barsky et al proposed the Flipper algorithm to mine a new type of pattern, named flipping patterns, based on the contrasting of correlations among items from different levels of abstractions (Barsky et al, 2011).…”
Section: Related Workmentioning
confidence: 99%
“…Vo and Le (2011) employed lattice and hash tables to mine ARs using a correlated measure from large databases. Baena-García and Morales-Bueno proposed an algorithm for mining frequent sequences with interestingness measures (Baena-García & Morales-Bueno, 2012). Barsky et al proposed the Flipper algorithm to mine a new type of pattern, named flipping patterns, based on the contrasting of correlations among items from different levels of abstractions (Barsky et al, 2011).…”
Section: Related Workmentioning
confidence: 99%
“…A wide range of interestingness measures are computed with positioning matrices in [14]. As an example, we can use the cells associated with the maximal proper prefix and maximal proper suffix, of a given factor, to obtain in constant time the associated supports, then, we can compute lift [16] from obtained supports.…”
Section: Sliding Positioningmentioning
confidence: 99%
“…SANSPOS algorithm can process strings with short tandem repeats efficiently. It has been successfully used to compute interestingness measures of string factors in order to generate datasets used to validate a methodology for interestingness measure mining [14].…”
Section: Introductionmentioning
confidence: 99%
“…Data mining is used to find patterns (or itemsets) hidden within data, and associations among the patterns. In particular, frequent pattern mining plays an essential role in many data mining tasks such as mining association rules [1], interesting measures [3,26], correlations [22,34], sequential patterns [8,29,41,42], constraint-based frequent patterns [5,40], graph patterns [35], emerging patterns [11,19,27] and approximate patterns [39]. Mining information and knowledge from very large databases is not easy since it takes a long time to process large datasets and the amount of discovered knowledge, and because the number of patterns can be significant and redundant.…”
Section: Introductionmentioning
confidence: 99%
“…Using this property, infrequent patterns can be pruned earlier. Frequent pattern mining has been studied in the area of data mining due to its broad applications in mining association rules [1], interesting measures or correlations [3,22,26,34], sequential patterns [8,29,35,41,42], constraint-based frequent patterns [5], graph patterns [35], emerging patterns [11,19,27] and other data mining tasks. These approaches have focused on enhancing the efficiency of algorithms in which techniques for search strategies, data structures, and data formats have been devised.…”
Section: Introductionmentioning
confidence: 99%