Non-derivable itemset mining
نویسندگان
چکیده
منابع مشابه
Depth-First Non-Derivable Itemset Mining
Mining frequent itemsets is one of the main problems in data mining. Much effort went into developing efficient and scalable algorithms for this problem. When the support threshold is set too low, however, or the data is highly correlated, the number of frequent itemsets can become too large, independently of the algorithm used. Therefore, it is often more interesting to mine a reduced collecti...
متن کاملMining Non-Derivable Association Rules
Association rule mining typically results in large amounts of redundant rules. We introduce efficient methods for deriving tight bounds for confidences of association rules, given their subrules. If the lower and upper bounds of a rule coincide, the confidence is uniquely determined by the subrules and the rule can be pruned as redundant, or derivable, without any loss of information. Experimen...
متن کاملMining All Non-derivable Frequent Itemsets
Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, ins...
متن کاملClosed Itemset Mining and Non-redundant Association Rule Mining
DEFINITION Let I be a set of binary-valued attributes, called items. A set X ⊆ I is called an itemset. A transaction database D is a multiset of itemsets, where each itemset, called a transaction, has a unique identifier, called a tid. The support of an itemset X in a dataset D, denoted sup(X), is the fraction of transactions in D where X appears as a subset. X is said to be a frequent itemset ...
متن کاملClosed Non-derivable Itemsets
Itemset mining typically results in large amounts of redundant itemsets. Several approaches such as closed itemsets, non-derivable itemsets and generators have been suggested for losslessly reducing the amount of itemsets. We propose a new pruning method based on combining techniques for closed and non-derivable itemsets that allows further reductions of itemsets. This reduction is done without...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2007
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-006-0054-6