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8,058
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
- DATA MINING AND KNOWLEDGE DISCOVERY
, 2004
"... Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still co ..."
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Cited by 1752 (64 self)
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Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still
Mininimum cardinality candidate generation
"... This paper describes the NGDE algorithm used for the 2009 DX Competition. This algorithm is based on new minimum cardinality candidate generator which exploits conflict set manipulation. 1 ..."
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This paper describes the NGDE algorithm used for the 2009 DX Competition. This algorithm is based on new minimum cardinality candidate generator which exploits conflict set manipulation. 1
gSpan: Graph-Based Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
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Cited by 650 (34 self)
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We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
- In ACL
, 2005
"... Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a co ..."
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Cited by 522 (15 self)
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Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a
Pattern Recognition for Candidate Generation in the Game of Shogi
- Proceedings of the Workshop on Computer Games (W31) at the IJCAI-97
, 1997
"... Chess has been an important research area in Artificial Intelligence for decades. It seems that the strongest programs will soon play better than the best experts, so it is time to look at other game domains as possible test domains for AI research. We feel that shogi is well suited for this, becaus ..."
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Cited by 2 (1 self)
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for pattern recognition to do this and give some preliminary results for candidate generation using pattern recognition. For candidate generation, our pattern recognition program currently reduces the number of candidates generated by at least 77% and generates the optimal move in more than 75% of the test
Training Products of Experts by Minimizing Contrastive Divergence
, 2002
"... It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual “expert ” models makes it hard to generate samples from the combined model but easy to infer the values of the l ..."
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Cited by 850 (75 self)
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of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation
Genetic Programming
, 1997
"... Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1056 (12 self)
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is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
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
Feature detection with automatic scale selection
- International Journal of Computer Vision
, 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
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Cited by 723 (34 self)
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scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is proposed for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ
Fast and Flexible MWE Candidate Generation with the mwetoolkit
"... We present an experimental environment for computer-assisted extraction of Multiword Expressions (MWEs) from corpora. Candidate extraction works in two steps: generation and filtering. We focus on recent improvements in the former, for which we increased speed and flexibility. We present examples th ..."
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We present an experimental environment for computer-assisted extraction of Multiword Expressions (MWEs) from corpora. Candidate extraction works in two steps: generation and filtering. We focus on recent improvements in the former, for which we increased speed and flexibility. We present examples
Results 1 - 10
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8,058