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LengthDecreasing Support Constraint ∗
"... Previous study has shown that mining frequent patterns with lengthdecreasing support constraint is very helpful in removing some uninteresting patterns based on the observation that short patterns will tend to be interesting if they have a high support, whereas long patterns can still be very inter ..."
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Previous study has shown that mining frequent patterns with lengthdecreasing support constraint is very helpful in removing some uninteresting patterns based on the observation that short patterns will tend to be interesting if they have a high support, whereas long patterns can still be very
Using LengthDecreasing Support Constraint
"... Over the years, a variety of algorithms for finding frequent sequential patterns in very large sequential databases have been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. In gene ..."
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function of their length. In this paper we present an algorithm called SLPMiner, that finds all sequential patterns that satisfy a lengthdecreasing support constraint. SLPMiner combines an efficient databaseprojectionbased approach for sequential pattern discovery with three effective database pruning
LPMiner: An Algorithm for Finding Frequent Itemsets Using LengthDecreasing Support Constraint
, 2001
"... Over the years, a variety of algorithms for finding frequent itemsets in very large transaction databases have been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. In general, items ..."
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Cited by 44 (23 self)
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of their length. In this paper we present an algorithm called LPMiner, that finds all itemsets that satisfy a lengthdecreasing support constraint. Our experimental evaluation shows that LPMiner is up to two orders of magnitude faster than the FPgrowth algorithm for finding itemsets at a constant support
Mining Sequential Patterns
, 1995
"... We are given a large database of customer transactions, where each transaction consists of customerid, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empiri ..."
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Cited by 1534 (7 self)
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We are given a large database of customer transactions, where each transaction consists of customerid, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem
Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach
 DATA MINING AND KNOWLEDGE DISCOVERY
, 2004
"... Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still co ..."
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Cited by 1700 (64 self)
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Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still
Finding Frequent Patterns Using LengthDecreasing Support Constraints
"... Finding prevalent patterns in large amount of data has been one of the major problems in the area of data mining. ..."
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Cited by 6 (0 self)
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Finding prevalent patterns in large amount of data has been one of the major problems in the area of data mining.
Mining Sequential Patterns: Generalizations and Performance Improvements
 Research Report RJ 9994, IBM Almaden Research
, 1995
"... Abstract. The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a user ..."
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Cited by 748 (5 self)
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Abstract. The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transactiontime, and each transaction is a set of items. The problem is to discover all sequential patterns with a user
Efficiently mining long patterns from databases
, 1998
"... We present a patternmining 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 465 (3 self)
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We present a patternmining 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
Constraint Networks
, 1992
"... Constraintbased reasoning is a paradigm for formulating knowledge as a set of constraints without specifying the method by which these constraints are to be satisfied. A variety of techniques have been developed for finding partial or complete solutions for different kinds of constraint expression ..."
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Cited by 1149 (43 self)
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Constraintbased reasoning is a paradigm for formulating knowledge as a set of constraints without specifying the method by which these constraints are to be satisfied. A variety of techniques have been developed for finding partial or complete solutions for different kinds of constraint
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
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Cited by 801 (1 self)
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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
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