Mining interestingness measures for string pattern mining

Baena-García, Manuel; Morales-Bueno, Rafael

doi:10.1016/j.knosys.2011.01.013

Cited by 10 publications

(5 citation statements)

References 18 publications

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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%

A General Method for mining high-Utility itemsets with correlated measures

Hùng

Tung

2021

Journal of Information and Telecommunication

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Discovering high-utility itemsets from a transaction database is one of the important tasks in High-Utility Itemset Mining (HUIM). The discovered high-utility itemsets (HUIs) must meet a user-defined given minimum utility threshold. Several methods have been proposed to solve the problem efficiently. However, they focused on exploring and discovering the set of HUIs. This research proposes a more generalized approach to mine HUIs using any user-specified correlated measure, named the General Method for Correlated High-utility itemset Mining (GMCHM). This proposed approach has the ability to discover HUIs that are highly correlated, based on the all_confidence and bond measures (and 38 other correlated measures). Evaluations were carried out on the standard datasets for HUIM, such as Accidents, BMS_utility and Connect. The results proved the high effectiveness of GMCHM in terms of running time, memory usage and the number of scanned candidates.

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Section: Related Workmentioning

confidence: 99%

A General Method for mining high-Utility itemsets with correlated measures

Hùng

Tung

2021

Journal of Information and Telecommunication

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“…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%

New data structures for analyzing frequent factors in strings

Baena-García

Morales-Bueno

2011

2011 11th International Conference on Intelligent Systems Design and Applications

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Discovering frequent factors from long strings is an important problem in many applications, such as biosequence mining. In classical approaches, the algorithms process a vast database of small strings. However, in this paper we analyze a small database of long strings. The main difference resides in the high number of patterns to analyze. To tackle the problem, we have developed a new algorithm for discovering frequent factors in long strings. This algorithm uses a new data structure to arrange nodes in a trie. A positioning matrix is defined as a new positioning strategy. By using positioning matrices, we can apply advanced prune heuristics in a trie with a minimal computational cost. The positioning matrices let us process strings including Short Tandem Repeats and calculate different interestingness measures efficiently. The algorithm has been successfully used in natural language and biological sequence contexts.

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“…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%