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Extracting Frequent Gradual Patterns Using Constraints Modeling [article]

Jerry Lonlac, Saïdd Jabbour, Engelbert Mephu Nguifo, Lakhdar Saïs, Badran Raddaoui
2019 arXiv   pre-print
In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset.  ...  This SAT-based declarative approach offers an additional possibility to benefit from the recent progress in satisfiability testing and to exploit the efficiency of modern SAT solvers for enumerating all  ...  However no satisfiability based approach has yet been proposed for the frequent gradual pattern mining problem.  ... 
arXiv:1903.08452v1 fatcat:fqw5rxh2zfclbac35fqqge7qpa

A Recent Overview: Rare Association Rule Mining

Urvi Y.Bhat, Pratik A. Patel
2014 International Journal of Computer Applications  
Rare association rule mining provides relationship between items which occurs uncommonly. This paper presents brief survey in the area of rare association rule mining.  ...  Traditional association mining methods generate frequent rules based on frequent itemsets with reference of minimum support and minimum confidence threshold which specified by user.  ...  We can say that, maximal item support value among all its item is satisfied for frequent pattern.  ... 
doi:10.5120/18848-9893 fatcat:afovtx7aebdihd6qd3vnlda4cu

Pushing Constraints to Generate Top-K Closed Sequential Graph Patterns

K. Vijay, K. Thammi, S. Sumalatha
2016 International Journal of Computer Applications  
Constraint-based mining is used in many fields of data mining such as frequent pattern mining, sequential pattern mining, and subgraph mining.  ...  In this paper, the problem of finding sequential patterns from graph databases is investigated. Two serious issues dealt in this paper are efficiency and effectiveness of mining algorithm.  ...  graph pattern mining is to find the top-k closed sequential graph patterns satisfying C.  ... 
doi:10.5120/ijca2016908818 fatcat:7avbvtvnfjaofbaxew4nxjnvtm

Looking into the seeds of time: Discovering temporal patterns in large transaction sets

2006 Information Sciences  
This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets.  ...  A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent.  ...  This paper precisely formulates the problem of mining temporal patterns for frequent itemsets in large transaction sets.  ... 
doi:10.1016/j.ins.2005.01.019 fatcat:sjuyfgeg45emdme2m4mvxyhil4

Open source data mining

Bart Goethals, Siegfried Nijssen, Mohammed J. Zaki
2005 SIGKDD Explorations  
for frequent pattern mining.  ...  Everybody who is working on frequent pattern mining problems has to deal with some of these issues.  ... 
doi:10.1145/1117454.1117476 fatcat:l4ucczocqrarvjt2cph67ufdl4

A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments

Fan Jiang, Carson Leung
2015 Algorithms  
Over the past few years, algorithms for handling big data according to a "systematic" view of the problem (e.g., MapReduce algorithms) are gaining momentum.  ...  Our algorithm uses the MapReduce model on a cloud environment for effective data analytics on these uncertain big data.  ...  Acknowledgments This project is partially supported by Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Manitoba.  ... 
doi:10.3390/a8041175 fatcat:l3nxtydm2ze6jgzymvq6kmevxe

Boolean satisfiability for sequence mining

Said Jabbour, Lakhdar Sais, Yakoub Salhi
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In this paper, we propose a SAT-based encoding for the problem of discovering frequent, closed and maximal patterns in a sequence of items and a sequence of itemsets.  ...  Then we introduce a new extension of the problem to enumerate patterns in a sequence of itemsets.  ...  The frequent pattern mining problem in a sequence of items (FPS) consists in computing the set M s of all the frequent patterns w.r.t. .  ... 
doi:10.1145/2505515.2505577 dblp:conf/cikm/JabbourSS13 fatcat:a5yzntjswjhhvplmryhrnagtjy


Nikhil Jamdar, A Vijayalakshmi
2017 Asian Journal of Pharmaceutical and Clinical Research  
Frequent pattern mining is a technique to find the frequently occurred items in data mining.  ...  The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints  ...  Frequent pattern mining gives the result on the basis of frequently occurrence of data [1] . It finds the pattern and gives the result for the data which occurs frequently [2] .  ... 
doi:10.22159/ajpcr.2017.v10s1.19634 fatcat:kz65l2dbhvgvzksefgjodqmbce

Pushing constraints into data streams

Andreia Silva, Cláudia Antunes
2013 Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '13  
However, one of the common criticisms pointed out to frequent pattern mining is the fact that it generates a huge number of patterns, independent of user expertise, making it very hard to analyze and use  ...  In this work we describe a set of strategies for pushing constraints into data stream mining, through the use of a pattern tree structure that captures a summary of the current possible patterns.  ...  PROBLEM STATEMENT Frequent pattern mining (PM) aims for enumerating all frequent patterns that conceptually represent relations among discrete entities (or items).  ... 
doi:10.1145/2501221.2501232 dblp:conf/kdd/SilvaA13 fatcat:ju7bzjvqzvfwxeq37bir2vuova

Frequent Pattern Mining and Current State of the Art

Kalli Srinivasa Nageswara Prasad, S. Ramakrishna
2011 International Journal of Computer Applications  
Identifying the association rules in large databases play a key role in data mining.  ...  There are two problems regarding this context, they are identifying all frequent item sets and to generate constraints from them.  ...  Frequent Pattern Mining Methodologies The initial algorithm suggested was AIS, by Agrawal et al. (1993) [1] for association rule mining problem.  ... 
doi:10.5120/3114-4279 fatcat:nza37yy2prft3jrueownimthw4


Syed Zishan Ali .
2013 International Journal of Research in Engineering and Technology  
Frequent pattern mining has become one of the most popular data mining approaches for the analysis of purchasing patterns.  ...  Mining Multi-level frequent pattern may lead to the discovery of mining patterns at different levels of hierarchy.  ...  Frequent pattern mining was first proposed by Agrawal [1] for market basket analysis in the form of association rule mining.  ... 
doi:10.15623/ijret.2013.0204017 fatcat:37rde4agl5ebzdxkhc5bqtgtoe


Duong Huy Tran, Thang Truong Nguyen, Thi Duc Vu, Anh The Tran
2018 Journal of Computer Science and Cybernetics  
Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real  ...  To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database.  ...  Candidate sequence patterns are built for the purpose of pruning the search space and still ensure downward closure property in the mining item interval normalized weighted frequent sequential patterns  ... 
doi:10.15625/1813-9663/34/3/13053 fatcat:mzzmmofe7ncejmn3f4bdzfzpcm

Mining Correlated Patterns with Multiple Minimum All-Confidence Thresholds [chapter]

R. Uday Kiran, Masaru Kitsuregawa
2013 Lecture Notes in Computer Science  
This paper theoretically analyzes the allconfidence measure, and shows that, although the measure satisfies the nullinvariant property, mining correlated patterns involving both frequent and rare items  ...  The cause for the problem is that the single minAllCon f threshold was not sufficient to capture the items' frequencies in a database effectively.  ...  If the items' frequencies vary a great deal, mining frequent patterns with a single minSup threshold leads to the dilemma known as the rare item problem [2] .  ... 
doi:10.1007/978-3-642-40319-4_26 fatcat:76pw3zid2vc7tpuy235byjfzby

Constraint-based Data Mining [chapter]

Jean-Francois Boulicaut, Baptiste Jeudy
2009 Data Mining and Knowledge Discovery Handbook  
This chapter emphasizes a real breakthrough for hard problems concerning local pattern mining under various constraints and it points out the current directions of research as well.  ...  Mining patterns turns to be the so-called inductive query evaluation process for which constraint-based Data Mining techniques have to be designed.  ...  ., 1997) for syntactic constraints on frequent itemsets, (Pasquier et al., 1999) for frequent and closed set mining, or (Garofalakis et al., 1999) for mining sequences that are both frequent and satisfy  ... 
doi:10.1007/978-0-387-09823-4_17 fatcat:xtpqy5tl35fnlj4sonqyx2pya4

A Proficient Approach of Incremental Algorithm for Frequent Pattern Mining

Endu Duneja, A.K. Sachan
2012 International Journal of Computer Applications  
The proposed algorithm can discover sequential frequent pattern itemsets in incremental database.  ...  Many researchers have recently focused on providing discrete solutions for these two problems.  ...  A fundamental problem for mining association rules is mining frequent itemsets.  ... 
doi:10.5120/7467-0596 fatcat:3xjfbnav7zdhbccgmqg6efo364
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