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A methodological approach for inferring causal relationships from opinions and news-derived events with an application to climate change. [PDF]
PeerJ Comput SciMarten J +3 more
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A quality measure for repeating multiple-unit spike patterns. [PDF]
Biol CybernPalm G +4 more
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Analysis of Acupoint Selection and Combination for Gouty Arthritis Treated with Moxibustion Based on Data Mining. [PDF]
Med AcupunctZhang J +9 more
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Development of targeted safety hazard management plans utilizing multidimensional association rule mining. [PDF]
HeliyonQiang X, Li G, Sari YA, Fan C, Hou J.
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Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery. [PDF]
Sci RepNaqash MA +7 more
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2012 IEEE 12th International Conference on Data Mining Workshops, 2012
Frequent Item set Mining (FISM) attempts to find large and frequent item sets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent(mixture property) and (ii) only a subset of items related to a customer intent
Shailesh Kumar +2 more
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Frequent Item set Mining (FISM) attempts to find large and frequent item sets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent(mixture property) and (ii) only a subset of items related to a customer intent
Shailesh Kumar +2 more
openaire +2 more sources
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016
Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item.
Hong Huang, Barry O'Sullivan
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Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item.
Hong Huang, Barry O'Sullivan
openaire +2 more sources
2012
In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets.
Jabbour, Said +3 more
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In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets.
Jabbour, Said +3 more
openaire +3 more sources