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1,538
An Efficient Algorithm for Mining Erasable Itemsets
"... Abstract. Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. Based on PID_list, we propose a new ..."
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Cited by 2 (0 self)
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Abstract. Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. Based on PID_list, we propose a
An Incremental Approach for Mining Erasable Itemsets
"... A factory has a production plan to produce products which are created from number of components and thus create profit. During financial crisis, the factory cannot afford to purchase all the necessary items as usual. Mining of erasable itemsets finds the itemsets which can be eliminated and do not g ..."
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A factory has a production plan to produce products which are created from number of components and thus create profit. During financial crisis, the factory cannot afford to purchase all the necessary items as usual. Mining of erasable itemsets finds the itemsets which can be eliminated and do
An Efficient Algorithm for Mining Erasable Itemsets Using the Difference of NC-Sets
"... Abstract—This paper proposes an improved version of the MERIT algorithm, dMERIT+, for mining all “erasable itemsets”. We first establish an algorithm MERIT+, a revised version of MERIT, which is then used as the foundation for dMERIT+. The proposed algorithm uses: a weight index, a hash table and th ..."
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Abstract—This paper proposes an improved version of the MERIT algorithm, dMERIT+, for mining all “erasable itemsets”. We first establish an algorithm MERIT+, a revised version of MERIT, which is then used as the foundation for dMERIT+. The proposed algorithm uses: a weight index, a hash table
A Parallel and Distributed Method to mine Erasable Itemsets from
"... High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. These profit values can be computed efficiently inorder to determine the gain of an itemset which in-t ..."
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-turn will help in production planning of any company. This gain value is needed to prune some of the irrelevant items from the high utility patterns at the time of economic crisis through erasable itemset mining. But all of the existing erasable itemset mining algorithms are based on centralized database
Dynamic Itemset Counting and Implication Rules for Market Basket Data
, 1997
"... We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We in ..."
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Cited by 615 (6 self)
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We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We
Discovering Frequent Closed Itemsets for Association Rules
, 1999
"... In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by lim ..."
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Cited by 410 (14 self)
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In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms
Efficiently mining long patterns from databases
, 1998
"... We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data ..."
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Cited by 457 (3 self)
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We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real
Beyond Market Baskets: Generalizing Association Rules To Dependence Rules
, 1998
"... One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: “A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market bask ..."
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Cited by 634 (6 self)
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-squared test for independence from classical statistics. This leads to a measure that is upward-closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between dependent and independent itemsets in the lattice. We develop pruning strategies based on the closure
CHARM: An efficient algorithm for closed itemset mining
, 2002
"... The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets usin ..."
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Cited by 320 (14 self)
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The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets
, 2000
"... Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces th ..."
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Cited by 312 (28 self)
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Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces
Results 1 - 10
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1,538
