@comment{{Bibliography by Michael Hahsler (http://wwwai.wu-wien.ac.at/~hahsler)}}
@comment{{This file has been generated by bib2bib 1.93}}
@comment{{Command line: /usr/bin/bib2bib -c 'category : "algorithm"' -ob tmp.bib association_rules_url.bib}}
@inproceedings{arules:Agrawal:1993,
author = {R. Agrawal and T. Imielinski and A. Swami},
title = {Mining Association Rules between Sets of Items in Large Databases},
booktitle = {Proceedings of the ACM SIGMOD International Conference on Management
of Data},
year = {1993},
pages = {207--216},
address = {Washington D.C.},
month = {May},
abstract = {Introduces association rules and the SUPPORT-CONFIDENCE framework
and an algorithm to mine large itemsets. The algorithm is sometimes
called AIS after the authors initials.},
category = {algorithm},
google = {http://www.google.com/search?q=%22Mining+Association+Rules+between+Sets+of+Items+in+Large+Databases%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Mining+Association+Rules+between+Sets+of+Items+in+Large+Databases%22}
}
@inproceedings{arules:Agrawal:1994,
author = {Rakesh Agrawal and Ramakrishnan Srikant},
title = {Fast Algorithms for Mining Association Rules in Large Databases},
booktitle = {Proceedings of the 20th International Conference on Very Large Data
Bases, {VLDB}},
year = {1994},
editor = {Jorge B. Bocca and Matthias Jarke and Carlo Zaniolo},
pages = {487--499},
address = {Santiago, Chile},
month = {September},
abstract = {Introduction of the APRIORI algorithm (the best-known algorithm; it
uses a breadth-first search strategy to counting the support of itemsets).
The algorithm uses an improved candidate generation function which
exploits the downward closure property of support and makes it more
efficient than AIS. Also an algorithm to generate synthetic transaction
data is presented. Such synthetic transaction data are widely used
for the evaluation and comparison of new algorithms.},
category = {algorithm, evaluation},
google = {http://www.google.com/search?q=%22Fast+Algorithms+for+Mining+Association+Rules+in+Large+Databases%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Fast+Algorithms+for+Mining+Association+Rules+in+Large+Databases%22}
}
@article{arules:Coenen:2004,
author = {Frans Coenen and Graham Goulbourne and Paul Leng},
title = {Tree structures for mining association rules},
journal = {Data Mining and Knowledge Discovery},
year = {2004},
volume = {8},
pages = {25--51},
abstract = {Describes how to compute PARTIAL SUPPORT COUNTS in one DB-pass and
how to store them in an enumeration tree (P-Tree).},
category = {algorithm},
google = {http://www.google.com/search?q=%22Tree+structures+for+mining+association+rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Tree+structures+for+mining+association+rules%22}
}
@article{arules:Han:2004,
author = {Jiawei Han and Jian Pei and Yiwen Yin and Runying Mao},
title = {Mining frequent patterns without candidate generation},
journal = {Data Mining and Knowledge Discovery},
year = {2004},
volume = {8},
pages = {53--87},
abstract = {Describes the data mining method FP-growth (frequent pattern growth)
which uses an extended prefix-tree (FP-tree) structure to store the
database in a compressed form. FP-growth adopts a divide-and-conquer
approach to decompose both the mining tasks and the databases. It
uses a pattern fragment growth method to avoid the costly process
of candidate generation and testing.},
category = {algorithm},
google = {http://www.google.com/search?q=%22Mining+frequent+patterns+without+candidate+generation%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Mining+frequent+patterns+without+candidate+generation%22}
}
@article{arules:Hipp:2000,
author = {Jochen Hipp and Ulrich G\"untzer and Gholamreza Nakhaeizadeh},
title = {Algorithms for Association Rule Mining -- {A} General Survey and Comparison},
journal = {SIGKDD Explorations},
year = {2000},
volume = {2},
pages = {1--58},
number = {2},
abstract = {Describes the fundamentals of association rule mining and presents
an systematization of existing algorithms.},
category = {algorithm},
google = {http://www.google.com/search?q=%22Algorithms+for+Association+Rule+Mining+--+A+General+Survey+and+Comparison%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Algorithms+for+Association+Rule+Mining+--+A+General+Survey+and+Comparison%22}
}
@inproceedings{arules:Mannila:1994,
author = {Heikki Mannila and Hannu Toivonen and A. Inkeri Verkamo},
title = {Efficient algorithms for discovering association rules},
booktitle = {{AAAI} Workshop on Knowledge Discovery in Databases (KDD-94)},
year = {1994},
editor = {Usama M. Fayyad and Ramasamy Uthurusamy},
pages = {181--192},
address = {Seattle, Washington},
publisher = {AAAI Press},
abstract = {Develop similar improvements to the candidate generation as APRIORI.
Itemsets with support are called covering sets. The paper also introduces
sampling from the database and gives bounds for the resulting estimate
of support.},
category = {algorithm,sampling},
google = {http://www.google.com/search?q=%22Efficient+algorithms+for+discovering+association+rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Efficient+algorithms+for+discovering+association+rules%22}
}
@inproceedings{arules:Orlando:2003,
author = {Salvatore Orlando and Claudio Lucchese and Paolo Palmerini and Raffaele
Perego and Fabrizio Silvestri},
title = {kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets},
booktitle = {FIMI'03: Proceedings of the IEEE ICDM Workshop on Frequent Itemset
Mining Implementations},
year = {2003},
editor = {Bart Goethals and Mohammed J. Zaki},
month = {November},
abstract = {Introduces the kDCI algorithm.},
category = {algorithm,implementation},
google = {http://www.google.com/search?q=%22kDCI:+a+Multi-Strategy+Algorithm+for+Mining+Frequent+Sets%22},
googlescholar = {http://scholar.google.com/scholar?q=%22kDCI:+a+Multi-Strategy+Algorithm+for+Mining+Frequent+Sets%22}
}
@inproceedings{arules:Savasere:1995,
author = {Ashok Savasere and Edward Omiecinski and Shamkant Navathe},
title = {An efficient algorithm for mining association rules in large databases},
booktitle = {Proceedings of the 21st VLDB Conference},
year = {1995},
pages = {432--443},
address = {Zurich, Switzerland},
abstract = {Introduction of the PARTITION algorithm. The database is scanned only
twice. For the first scan the DB is partitioned and in each partition
support is counted. Then the counts are merged to generate potential
large itemsets. In the second scan the potential large itemsets are
counted to find the actual large itemsets.},
category = {algorithm},
google = {http://www.google.com/search?q=%22An+efficient+algorithm+for+mining+association+rules+in+large+databases%22},
googlescholar = {http://scholar.google.com/scholar?q=%22An+efficient+algorithm+for+mining+association+rules+in+large+databases%22}
}
@inproceedings{arules:Toivonen:1996,
author = {Hannu Toivonen},
title = {Sampling Large Databases for Association Rules},
booktitle = {VLDB '96: Proceedings of the 22th International Conference on Very
Large Data Bases},
year = {1996},
pages = {134--145},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
abstract = {Find frequent itemsets in a random sample of a database (that fits
into main memeory) and then verify the found frequent itemsets in
the database.},
category = {algorithm,sampling},
isbn = {1-55860-382-4},
google = {http://www.google.com/search?q=%22Sampling+Large+Databases+for+Association+Rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Sampling+Large+Databases+for+Association+Rules%22}
}
@article{arules:Zaki:2000,
author = {Mohammed J. Zaki},
title = {Scalable Algorithms for Association Mining},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2000},
volume = {12},
pages = {372--390},
number = {3},
month = {May/June},
abstract = { Introduces six new algorithms combining several features (database
format, the decomposition technique, and the search procedure). Includes
Eclat (Equivalence CLAss Transformation), MaxEclat, Clique, MaxClique,
TopDown, and AprClique. ECLAT is a well known depth-first search
algorithm using set intersection.},
category = {algorithm},
google = {http://www.google.com/search?q=%22Scalable+Algorithms+for+Association+Mining%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Scalable+Algorithms+for+Association+Mining%22}
}
@article{arules:Han:2007,
author = {Han, J. and Cheng, H. and Xin, D. and Yan, X. },
journal = {Data Mining and Knowledge Discovery},
number = {1},
title = {Frequent Pattern Mining: Current Status and Future Directions},
volume = {14},
year = {2007},
abstract = {Complete overview of the state-of-the art in frequent patten mining and identifies future research directions.},
category = {algorithm, concise, sequential},
google = {http://www.google.com/search?q=%22Frequent+Pattern+Mining:+Current+Status+and+Future+Directions%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Frequent+Pattern+Mining:+Current+Status+and+Future+Directions%22}
}
This file was generated by bibtex2html 1.93.