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Multi-Sorted Inverse Frequent Itemsets Mining: On-Going Research

Domenico Saccà, Edoardo Serra, Antonio Piccolo
2016 Alberto Mendelzon Workshop on Foundations of Data Management  
Inverse frequent itemset mining (IFM) consists of generating artificial transactional databases reflecting patterns of real ones, in particular, satisfying given frequency constraints on the itemsets.  ...  Given R, Σ = Σ ∪ Σ, and an integer size > 0, the multi-sorted inverse frequent itemset mining problem, shortly denoted as ms-IFM, consists of finding a many-sorted dataset D on R such that both δ D = size  ...  In this paper we propose to extend inverse frequent itemset mining (IFM) that consists of generating a transactional database satisfying given support constraints on the itemsets in an input set, that  ... 
dblp:conf/amw/SaccaSP16 fatcat:zi5k2eyzszbevbongwhrsmmjqu

Multi-Sorted Inverse Frequent Itemsets Mining [article]

Domenico Sacca', Edoardo Serra, Pietro Dicosta, Antonio Piccolo
2013 arXiv   pre-print
on the itemsets in an input set, that are typically the frequent ones.  ...  A first step in this direction is to use inverse mining techniques such as inverse frequent itemset mining (IFM) that consists of generating a transactional database satisfying given support constraints  ...  IFM Problem In this section we provide a general formulation of the many-sorted inverse frequent itemsets mining problem.  ... 
arXiv:1310.3939v1 fatcat:aj54nykpfzakbkenmpl63fx3fy

Output privacy in data mining

Ting Wang, Ling Liu
2011 ACM Transactions on Database Systems  
This paper presents a systematic study on the problem of protecting output privacy in data mining, and particularly, stream mining: (i) we highlight the importance of this problem by showing that even  ...  In general, privacy preservation in data mining demands protecting both input and output privacy: the former refers to sanitizing the raw data itself before performing mining; while the latter refers to  ...  ACKNOWLEDGEMENTS This work is partially sponsored by grants from NSF CyberTrust, NSF NetSE, an IBM SUR grant, and a grant from Intel Research Council.  ... 
doi:10.1145/1929934.1929935 fatcat:hjxhddlkr5fcrdafcx4gyzgjcy

Mining@home: toward a public-resource computing framework for distributed data mining

C. Lucchese, C. Mastroianni, S. Orlando, D. Talia
2009 Concurrency and Computation  
In particular, we focus on one of the main data mining problems: the extraction of closed frequent itemsets from transactional databases.  ...  This paper introduces a general framework for distributed data mining applications called Mining@home.  ...  ACKNOWLEDGEMENTS This research work has been partially carried out under the Network of Excellences CoreGRID (FP6 contract no. IST-FP6-004265) and S-Cube (FP7 contract no.  ... 
doi:10.1002/cpe.1545 fatcat:2ccaq3kjpvbnjmaqexmxdzruya

Mining Frequent Itemsets: a Formal Unification [article]

Slimane Oulad-Naoui, Hadda Cherroun, Djelloul Ziadi
2020 arXiv   pre-print
It is generally well agreed that developing a unifying theory is one of the most important issues in Data Mining research.  ...  In the last two decades, a great deal of work has been devoted to the algorithmic aspects of the Frequent Itemset (FI) Mining problem. We are motivated by the need for formal modeling in the field.  ...  INTRODUCTION Mining Frequent Itemsets (FI) is an important problem in Data Mining (DM).  ... 
arXiv:1502.02642v4 fatcat:5jryhi5fcvcudo2wti5dknnwbm

Butterfly: Protecting Output Privacy in Stream Mining

Ting Wang, Ling Liu
2008 2008 IEEE 24th International Conference on Data Engineering  
This work studies the problem of protecting output privacy in the context of frequent pattern mining over data streams.  ...  Privacy preservation in data mining demands protecting both input and output privacy. The former refers to sanitizing the raw data itself before performing mining.  ...  ACKNOWLEDGMENT This research is partially sponsored by grants from NSF CyberTrust, an IBM SUR grant, and an IBM faculty award.  ... 
doi:10.1109/icde.2008.4497526 dblp:conf/icde/WangL08 fatcat:flw4m2t2nfgfxlz4jcqublizde

Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Gergely Honti, János Abonyi
2021 Mathematics  
The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.  ...  The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis.  ...  Frequent Itemset Mining in Multidimensional Networks Frequent itemset mining (FIM) is a mining technique used to uncover frequent correlations in transactional datasets [48] .  ... 
doi:10.3390/math9040450 fatcat:w2kbidjmqbhjhneoikfl3gvv2m

MITHRIL: Mining Sporadic Associations for Cache Prefetching [article]

Juncheng Yang, Reza Karimi, Trausti Sæmundsson, Avani Wildani, Ymir Vigfusson
2017 arXiv   pre-print
MITHRIL is inspired by sporadic association rule mining and only relies on the timestamps of requests.  ...  The growing pressure on cloud application scalability has accentuated storage performance as a critical bottle- neck.  ...  Sporadic Association Rule Mining Frequent itemset mining aims to discover which items co-occur frequently in a transaction database.  ... 
arXiv:1705.07400v1 fatcat:zoj7chgrore7poh62jqfeqlrwu

EBPA: An efficient data structure for frequent closed itemset mining

C. Vajiramedhin, J. Werapun
2013 Applied Mathematical Sciences  
Practically, many data structures were proposed to maintain valuable data for frequent closed itemset mining (FCIM), while each data structure has its own advantages and disadvantages.  ...  In closed itemset mining, the process of mining from a large transaction database directly often leads to inefficient space and time.  ...  EBPA: an efficient data structure for frequent closed itemset mining  ... 
doi:10.12988/ams.2013.13135 fatcat:qrmnxy62ifdhdfi6tpfckat2sa

Semantic Pattern Mining Based Web Service Recommendation [chapter]

Hafida Naïm, Mustapha Aznag, Nicolas Durand, Mohamed Quafafou
2016 Lecture Notes in Computer Science  
We define the notion of semantic patterns which are maximal frequent itemsets of topics.  ...  These sets of services are then used to recommend services in the on-line process.  ...  We propose a content-based recommendation system leveraging probabilistic topic models and pattern mining (more precisely, maximal frequent itemset mining).  ... 
doi:10.1007/978-3-319-46295-0_26 fatcat:pkuqakkbongabdzp5cho4pbbuq

TopCat: data mining for topic identification in a text corpus

C. Clifton, R. Cooley, J. Rennie
2004 IEEE Transactions on Knowledge and Data Engineering  
Frequent itemsets are generated from the groups of items, followed by clusters formed with a hypergraph partitioning scheme.  ...  This paper presents a novel method for identifying related items based on "traditional" data mining techniques.  ...  Frequent itemset computation Computing frequent itemsets to 76 minutes.  ... 
doi:10.1109/tkde.2004.32 fatcat:kogmhh2ubrbyjktl32qaalxz6e

TopCat: Data Mining for Topic Identification in a Text Corpus [chapter]

Chris Clifton, Robert Cooley
1999 Lecture Notes in Computer Science  
Frequent itemsets are generated from the groups of items, followed by clusters formed with a hypergraph partitioning scheme.  ...  This paper presents a novel method for identifying related items based on "traditional" data mining techniques.  ...  Frequent itemset computation Computing frequent itemsets to 76 minutes.  ... 
doi:10.1007/978-3-540-48247-5_19 fatcat:ut3lq3fjcndvhbjixwfgszx4ve

An information-theoretic approach to quantitative association rule mining

Yiping Ke, James Cheng, Wilfred Ng
2007 Knowledge and Information Systems  
We find that the cliques in the MI graph represent a majority of the frequent itemsets.  ...  Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules  ...  As an on-going work, we consider to incorporate the concept of nearclique, which is a clique except for one edge, for computing frequent itemsets into our framework.  ... 
doi:10.1007/s10115-007-0104-4 fatcat:6scsfelcxrernbmdoit36yfoze

Discovering Strategic Behaviors in Multi-Agent Scenarios by Ontology-Driven Mining [chapter]

Davide Bacciu, Andrea Bellandi, Barbara Furletti, Valerio Grossi, Andrea Romei
2008 Advances in Robotics, Automation and Control  
Next, L 1 is used to find the set of frequent 2-itemsets L 2 , and so on until all the frequent patterns have been discovered.  ...  Apriori has been the first frequent itemset algorithm to be proposed (Agrawal et al., 1994) . It exploits a bottom-up, level-wise exploration of the lattice of frequent itemsets.  ...  Discovering Strategic Behaviors in Multi-Agent Scenarios by Ontology-Driven Mining, Advances in Robotics, Automation and Control, Jesus Aramburo and Antonio Ramirez Trevino (Ed.), ISBN: 978-953-7619-16  ... 
doi:10.5772/5529 fatcat:qkacis2w7zdplczbk7clpmgbba

Heavyweight Pattern Mining in Attributed Flow Graphs

Carolina Simoes Gomes, Jose Nelson Amaral, Joerg Sander, Joran Siu, Li Ding
2014 2014 IEEE International Conference on Data Mining  
The principle of strategy is from one thing, to know ten thousand things." -Miyamoto Musashi, in The Book of Five Rings.  ...  If you master the principles of sword-fencing, when you freely beat one man, you beat any man in the world. The spirit of defeating a man is the same for ten million men.  ...  In frequent-itemset mining, the more frequent an itemset is in the database, the more interesting the itemset is considered to be.  ... 
doi:10.1109/icdm.2014.51 dblp:conf/icdm/GomesASSD14 fatcat:kvxyxu6mbvb73pqj4fpez6h5o4
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