Multi-sorted inverse frequent itemsets mining: On-going research

Domenico Sacc'a; Edoardo Serra; Antonio Piccolo

Multi-sorted inverse frequent itemsets mining: On-going research

Domenico Sacc'a, Edoardo Serra, Antonio Piccolo

Computer Science Department

University of Calabria

Research output: Contribution to journal › Conference article › peer-review

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Abstract

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. An extension of IFM called manysorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics.

Original language	American English
Journal	CEUR Workshop Proceedings
Volume	1644
State	Published - 2016
Event	10th Alberto Mendelzon International Workshop on Foundations of Data Management, AMW 2016 - Panama City, Panama Duration: 8 May 2016 → 10 May 2016

Keywords

Big Data Synthesized Datasets
Frequent Itemsets
Inverse data mining

EGS Disciplines

Computer Sciences

Access to Document

https://scholarworks.boisestate.edu/cs_facpubs/69License: Other

Cite this

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title = "Multi-sorted inverse frequent itemsets mining: On-going research",

abstract = "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. An extension of IFM called manysorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics.",

keywords = "Big Data Synthesized Datasets, Frequent Itemsets, Inverse data mining",

author = "Domenico Sacc'a and Edoardo Serra and Antonio Piccolo",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 for the individual papers by the papers's authors.; 10th Alberto Mendelzon International Workshop on Foundations of Data Management, AMW 2016 ; Conference date: 08-05-2016 Through 10-05-2016",

year = "2016",

language = "American English",

volume = "1644",

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TY - JOUR

T1 - Multi-sorted inverse frequent itemsets mining

T2 - 10th Alberto Mendelzon International Workshop on Foundations of Data Management, AMW 2016

AU - Sacc'a, Domenico

AU - Serra, Edoardo

AU - Piccolo, Antonio

PY - 2016

Y1 - 2016

N2 - 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. An extension of IFM called manysorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics.

AB - 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. An extension of IFM called manysorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics.

KW - Big Data Synthesized Datasets

KW - Frequent Itemsets

KW - Inverse data mining

UR - https://www.scopus.com/pages/publications/84985903291

M3 - Conference article

SN - 1613-0073

VL - 1644

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 8 May 2016 through 10 May 2016

ER -

Multi-sorted inverse frequent itemsets mining: On-going research

Abstract

Keywords

EGS Disciplines

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