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A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set
2016
International Journal of Computer Applications
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Association Rule mining is a sub-discipline of data mining. Apriori algorithm is one of the most popular association rule mining technique. ...
extract maximal frequent itemset. ...
To calculate the frequency count of ith itemset
BT[mid,i], the length of Longest common subsequence (LCS)
between it and every transaction in TT is evaluated one by
one and if the length of longest ...
doi:10.5120/ijca2016908883
fatcat:lvfgi34nzzc6pnete4atcyz7eq
An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function
2010
International Journal of Computer Applications
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We developed an algorithm (termed DFPMT-A Dynamic Approach for Frequent Patterns Mining Using Transposition of Database) for mining frequent patterns which are based on Apriori algorithm and used Dynamic ...
Frequent patterns mining is the focused research topic in association rule analysis. Apriori algorithm is a classical algorithm of association rule mining. ...
K K Shrivastava for discussing and giving us advice on its implementation. ...
doi:10.5120/1410-1904
fatcat:vgyxjuaz7va6ndpseed64wt5c4
On efficiently summarizing categorical databases
2005
Knowledge and Information Systems
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However, most of the frequent itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to identify a subset of the most promising ones that can be used ...
Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. ...
To achieve this goal, our solution to this problem formulation is that for each transaction we find one of the longest frequent itemsets that it contains and use this longest frequent itemset as the corresponding ...
doi:10.1007/s10115-005-0216-7
fatcat:kojdvgqlivc7nizel7twtslswa
Efficiently mining long patterns from databases
1998
SIGMOD record
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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. ...
, and Dao-I Lin for sharing details of the experiments used to evaluate Pincer-Search. ...
doi:10.1145/276305.276313
fatcat:zkssnbomwra2xacqvsrxztngnq
Efficiently mining long patterns from databases
1998
Proceedings of the 1998 ACM SIGMOD international conference on Management of data - SIGMOD '98
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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. ...
, and Dao-I Lin for sharing details of the experiments used to evaluate Pincer-Search. ...
doi:10.1145/276304.276313
dblp:conf/sigmod/Bayardo98
fatcat:vu7yxs64k5b35jercnnlgrt6zi
Discovery of maximum length frequent itemsets
2008
Information Sciences
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Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. ...
The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. ...
Then we review some related research on frequent itemset mining. ...
doi:10.1016/j.ins.2007.08.006
fatcat:7zd35qjfczd3tiufhourjj6acu
A comprehensive method for discovering the maximal frequent set
2012
IOSR Journal of Computer Engineering
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is to generate reliable association rules based on all frequent itemsets found in the first step. ...
Identifying all frequent itemsets in a large database dominates the overall performance in the association rule mining. ...
, the improvement is not clear when the length of the longest frequent itemset is relatively short. ...
doi:10.9790/0661-0743139
fatcat:sdno7ecewrbwhlwrx3a6aadiry
Generating non-redundant association rules
2000
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00
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We present a new framework for associations based on the concept of closed frequent itemsets. ...
The number of non-redundant rules produced by the new approach is exponentially (in the length of the longest frequent itemset) smaller than the rule set from the traditional approach. ...
At 40% support, the longest frequent itemset has length 7. ...
doi:10.1145/347090.347101
dblp:conf/kdd/Zaki00
fatcat:cx5mvmphwnbd7h464h42r2nyka
Extracting Frequent Gradual Patterns Using Constraints Modeling
[article]
2019
arXiv
pre-print
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We show the practical feasibility of our SAT model by running experiments on two real world datasets. ...
In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset. ...
The second one, allows us for a given gradual itemset s, to place uniquely one transaction in each position of the longest sequence of transactions respecting s. ...
arXiv:1903.08452v1
fatcat:fqw5rxh2zfclbac35fqqge7qpa
Cardinality Statistics Based Maximal Frequent Itemsets Mining
[chapter]
2012
Communications in Computer and Information Science
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The only recourse is to mine the maximal frequent itemsets in the domain with very long patterns. ...
Extracting frequent itemsets is an important task in many data mining applications. ...
The first one is to mine only the maximal frequent itemsets [1, 6, 8, 9, 15] . ...
doi:10.1007/978-3-642-29166-1_3
fatcat:rzte5dbt4fdufkevidff6qd32i
PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets
2010
2010 IEEE International Conference on Data Mining
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We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent ...
gradual itemsets. ...
Introduction Frequent pattern mining is the component of data mining focused on extracting patterns that occur frequently in data. ...
doi:10.1109/icdm.2010.101
dblp:conf/icdm/DoLT10
fatcat:hnenn5b47fh6dh4dsh4dfsmali
PADS: a simple yet effective pattern-aware dynamic search method for fast maximal frequent pattern mining
2008
Knowledge and Information Systems
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While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent patterns efficiently is important in both theory and applications of frequent pattern mining. ...
The source code and the executable code (on both Windows and Linux platforms) are publicly available at ...
Thus, the search space of max-pattern mining is the lattice of itemsets consisting of only frequent items, which is called the itemset lattice. ...
doi:10.1007/s10115-008-0179-6
fatcat:zptyu23tvnckdakeflproqhiee
Frequent Pattern Discovery Without Binarization: Mining Attribute Profiles
[chapter]
2006
Lecture Notes in Computer Science
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In this paper we propose to overcome this limitation by introducing the concept of mining frequent attribute profiles that describes the relationships between the original attributes. ...
Frequent pattern discovery has become a popular solution to many scientific and industrial problems in a range of different datasets. ...
Based on the mining strategy by which frequent itemsets are discovered, two types of algorithm can be distinguished: breadth-first search and depth-first search. ...
doi:10.1007/11871637_52
fatcat:doy4zrf4hfdnxckq3lhawtkaay
Efficient Parallel Mining of Gradual Patterns on Multicore Processors
[chapter]
2012
Studies in Computational Intelligence
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However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. ...
In this paper, we thus propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE) on multicore processors. ...
Acknowledgements The authors would like to acknowledge Lisa Di Jorio for providing the source code of the implementation of the GRITE algorithm [Di Jorio et al., 2009] . ...
doi:10.1007/978-3-642-25838-1_8
fatcat:ezet7xkfgrgyrld4zrujqzt3qq
Towards a new approach for mining frequent itemsets on data stream
2006
Journal of Intelligent Information Systems
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Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. ...
In this paper we propose a new approach for mining itemsets. ...
In this paper, we propose a new approach, called Fids (Frequent itemsets mining on data streams). ...
doi:10.1007/s10844-006-0002-3
fatcat:eyhcuaebdfbfliptnfr44gis2y
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