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A Matrix based Maximal Frequent Itemset Mining Algorithm without Subset Creation

Balwant Kumar, Dharmender Kumar
2017 International Journal of Computer Applications  
Present paper proposes a new top down approach based on compressed matrix for mining maximal frequent itemsets directly without the help of subset.  ...  This process is time intense because these algorithms first mine the minimal frequent itemsets and then generate maximal frequent itemsets from minimal frequent itemsets.  ...  HK Jnanamurthy et al. presented a top down approach to find maximal frequent itemset by using subset named as MFIF [12] .  ... 
doi:10.5120/ijca2017912963 fatcat:athoxyosnjaxrbj4z3yudu4niy

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.  ...  Thanks to the flexibility and to the declarative aspects of our SAT-based approach, an encoding for the sequences of itemsets is obtained by a very slight modification of that for the sequences of items  ...  They follows the constraint programing (CP) based approach proposed recently by Luc De Raedt et al. in [10] for itemset mining.  ... 
doi:10.1145/2505515.2505577 dblp:conf/cikm/JabbourSS13 fatcat:a5yzntjswjhhvplmryhrnagtjy

On When and How to use SAT to Mine Frequent Itemsets [article]

Rui Henriques and Inês Lynce and Vasco Manquinho
2012 arXiv   pre-print
This work deepens the study on when and how to use SAT for the frequent itemset mining (FIM) problem by defining different encodings with multiple task-driven enumeration options and search strategies.  ...  A new stream of research was born in the last decade with the goal of mining itemsets of interest using Constraint Programming (CP).  ...  Each iteration returns a maximal frequent itemset, starting with the longest maximal frequent itemset until reaching the shortest maximal frequent itemset.  ... 
arXiv:1207.6253v1 fatcat:s5a3igmhtzb67cmiber6wqmge4

Standing Out in a Crowd: Selecting Attributes for Maximum Visibility

Muhammed Miah, Gautam Das, Vagelis Hristidis, Heikki Mannila
2008 2008 IEEE 24th International Conference on Data Engineering  
Our exact algorithms are based on Integer Programming (IP) formulations of the problems, as well as on adaptations of maximal frequent itemset mining algorithms, while our approximation algorithms are  ...  ., buyers searching for products in a catalog).  ...  The work of Heikki Mannila was partially supported by the Academy of Finland.  ... 
doi:10.1109/icde.2008.4497444 dblp:conf/icde/MiahDHM08 fatcat:rfvkudznengm3f6642miuoe7ua

Experiences of Using a Quantitative Approach for Mining Association Rules [chapter]

Liang Dong, Christos Tjortjis
2003 Lecture Notes in Computer Science  
This paper proposes an enhancement with a memory efficient data structure of a quantitative approach to mine association rules from data.  ...  The best features of the three algorithms (the Quantitative Approach, DHP, and Apriori) were combined to constitute our proposed approach.  ...  by using the Boolean algorithm to create the frequent itemsets.  ... 
doi:10.1007/978-3-540-45080-1_93 fatcat:zdvs4aasabgcpithlivm2txgwe

Determining Attributes to Maximize Visibility of Objects

M. Miah, G. Das, V. Hristidis, H. Mannila
2009 IEEE Transactions on Knowledge and Data Engineering  
Another class of exact methods is based on maximal frequent itemset mining algorithms. The approximation algorithms are based on greedy heuristics.  ...  Index Terms-Data mining, knowledge and data engineering tools and techniques, marketing, mining methods and algorithms, retrieval models. --------------------  ...  A known approach for mining maximal frequent itemsets is the complete random walk [12] , which is a bottom-up approach.  ... 
doi:10.1109/tkde.2009.72 fatcat:eavx2lkgmvg27mlqn65qema65u

Mining-based compression approach of propositional formulae

Said Jabbour, Lakhdar Sais, Yakoub Salhi, Takeaki Uno
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
It combines both frequent itemset mining techniques and Tseitin's encoding for a compact representation of CNF formulae.  ...  In this paper, we propose a first application of data mining techniques to propositional satisfiability.  ...  I is called maximal when for all I 0 I, I 0 / 2 FIM(D, ) (I 0 is not a frequent itemset). We denote by MAX (D, ) the set of all maximal frequent itemsets in D with as a minimal support threshold.  ... 
doi:10.1145/2505515.2505576 dblp:conf/cikm/JabbourSSU13 fatcat:nhband3rvbf5vibzcwrdkzzw6m

Itemset Mining as a Challenge Application for Answer Set Enumeration [chapter]

Matti Järvisalo
2011 Lecture Notes in Computer Science  
We evaluate a simple ASP-based approach experimentally and compare it to a recently proposed framework exploiting constraint programming (CP) solvers for itemset mining.  ...  Itemset Mining Assume a set I = {1, ..., m} of items and a set T = {1, ..., n} of transactions. Intuitively, a transaction t ∈ T consists of a subset of items from I. An itemset database ⋆  ...  Maximal Frequent Itemsets.  ... 
doi:10.1007/978-3-642-20895-9_35 fatcat:pvjahmho65dkjd32gln55omzau

Closed Frequent Itemset Mining with Arbitrary Side Constraints

Gokberk Kocak, Ozgur Akgun, Ian Miguel, Peter Nightingale
2018 2018 IEEE International Conference on Data Mining Workshops (ICDMW)  
This paper presents a constraint programming based approach that combines arbitrary side constraints with closed frequent itemset mining.  ...  Our approach allows arbitrary side constraints to be expressed in a high level and declarative language which is then translated automatically for efficient solution by a SAT solver.  ...  Our approach can be applied to maximal frequent itemset mining with a minor change to a specification. No programming whatsoever is required.  ... 
doi:10.1109/icdmw.2018.00175 dblp:conf/icdm/KocakAMN18 fatcat:4gohb7wphzbnxf7e4fygcw2fqq

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.  ...  frequent gradual patterns in a numerical dataset.  ...  Several satisfiability based approach have been proposed for the classical patterns mining problem such that mining frequent itemsets in transactional data, mining frequent sequence in a data-sequence.  ... 
arXiv:1903.08452v1 fatcat:fqw5rxh2zfclbac35fqqge7qpa

Efficient Analysis of Pattern and Association Rule Mining Approaches

Thabet Slimani, Amor Lazzez
2014 International Journal of Information Technology and Computer Science  
The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules.  ...  algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining.  ...  mining, given a minimum support threshold:  Closed frequent Itemset: An itemset X is a closed frequent itemset in set S if X is both closed and frequent in S.  Maximal frequent itemset :An itemset X  ... 
doi:10.5815/ijitcs.2014.03.09 fatcat:azuo5zey35flrc3disyiersjnu

Constraint Programming for Mining Borders of Frequent Itemsets

Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
Frequent itemset mining is one of the most studied tasks in knowledge discovery. It is often reduced to mining the positive border of frequent itemsets, i.e. maximal frequent itemsets.  ...  We propose a generic framework based on constraint programming to mine both borders of frequent itemsets.One can easily decide which border to mine by setting a simple parameter.  ...  We build the dataset D on the set I = (a 1 , . . As in all CP models for itemset mining, there is a Boolean variable for each item.  ... 
doi:10.24963/ijcai.2019/149 dblp:conf/ijcai/BelaidBL19 fatcat:mvm7jsgnmvavxlbb4pa2i2j6cm

Integrating Constraint Programming and Itemset Mining [chapter]

Siegfried Nijssen, Tias Guns
2010 Lecture Notes in Computer Science  
by specialized itemset mining systems, which could discourage their use.  ...  which demonstrates that it also is a promising approach for analyzing pattern mining tasks from more theoretical perspectives.  ...  This work was supported by a Postdoc and a project grant from the Research Foundation-Flanders, project "Principles of Patternset Mining", as well as a grant from the Institute for the Promotion and Innovation  ... 
doi:10.1007/978-3-642-15883-4_30 fatcat:klvpevtfr5fc7e2zz4chy2yjse

Computational Complexity of Three Central Problems in Itemset Mining [article]

Christian Bessiere, Mohamed-Bachir Belaid, Nadjib Lazaar
2020 arXiv   pre-print
We prove that mining confident rules with a given item in the head is NP-hard. We prove that mining high utility itemsets is NP-hard.  ...  We finally prove that mining maximal or closed itemsets is coNP-hard as soon as the users can specify constraints on the kind of itemsets they are interested in.  ...  We can cite the algorithm CHARM for mining maximal frequent itemsets (Zaki and Hsiao, 2002) , or LCM for mining closed frequent itemsets (Uno et al., 2004) .  ... 
arXiv:2012.02619v3 fatcat:m4sruzjcjveqbgvzwxp7cejlam

Modeling and Mining Optimal Patterns Using Dynamic CSP

Willy Ugarte, Patrice Boizumault, Samir Loudni, Bruno Cremilleux
2015 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)  
Then, we propose a generic method based on a Dynamic Constraint Satisfaction Problem to mine OPs, and we show that any OP is characterized by a basic constraint and a set of constraints to be dynamically  ...  We introduce the notion of Optimal Patterns (OPs), defined as the best patterns according to a given user preference, and show that OPs encompass many data mining problems.  ...  This work was supported by the National Agency of Research Hybride ANR-11-BS02-002 project.  ... 
doi:10.1109/ictai.2015.19 dblp:conf/ictai/UgarteBLC15 fatcat:bqq3qpuzmvbmzcliby4uafd464
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