datafusion

Data Fusion (open-access research monograph, 2015)

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 3 DOI reference(s) in README
✓
Academic publication links
Links to: scholar.google
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (9.1%) to scientific vocabulary

Keywords

aggregation data fusion fuzzy-logic mean multidimensional-analysis multidimensional-data spread statistics strings variance

Last synced: 6 months ago · JSON representation ·

Repository

Data Fusion (open-access research monograph, 2015)

Basic Info

Host: GitHub
Owner: gagolews
License: other
Language: R
Default Branch: master
Homepage:
Size: 4.78 MB

Statistics

Stars: 0
Watchers: 2
Forks: 1
Open Issues: 0
Releases: 0

Topics

aggregation data fusion fuzzy-logic mean multidimensional-analysis multidimensional-data spread statistics strings variance

Created almost 4 years ago · Last pushed over 3 years ago

Metadata Files

Readme License Citation

Data Fusion: Theory, Methods, and Applications

An open-access research monograph by Marek Gagolewski (download PDF)

A proper fusion of complex data is of interest to many researchers in diverse fields, including computational statistics, computational geometry, bioinformatics, machine learning, pattern recognition, quality management, engineering, statistics, finance, economics, etc. It plays a crucial role in:

synthetic description of data processes or whole domains,
creation of rule bases for approximate reasoning tasks,
reaching consensus and selection of the optimal strategy in decision support systems,
imputation of missing values,
data deduplication and consolidation,
record linkage across heterogeneous databases,
clustering.

Furthermore, many useful machine learning methods are based on a proper aggregation of information entities. In particular, the class of ensemble methods for classification is very successful because of the assumption that no single "weak" classifier can perform as nicely as the whole group. Neural networks and other deep learning tools can be understood as hierarchies of individual fusion functions. Appropriate data fusion is crucial for privacy reasons as well (think: GDPR).

This open-access research monograph integrates the spread-out results from different domains using the methodology of the well-established classical aggregation framework, introduces researchers and practitioners to Aggregation 2.0, as well as points out the challenges and interesting directions for further research.

Gagolewski M., Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 2015, 290 pp., ISBN: 978-83-63159-20-7, DOI: 10.5281/zenodo.6960306.

Reviewers: Gleb Beliakov and Radko Mesiar.

Owner

Name: Marek Gagolewski
Login: gagolews
Kind: user
Location: Melbourne, VIC, Australia
Company: Deakin University

Website: https://www.gagolewski.com
Repositories: 23
Profile: https://github.com/gagolews

Free universities!

Citation (CITATION.cff)

cff-version: 1.2.0
message: "Please cite this book as below."
title: "Data Fusion: Theory, Methods, and Applications"
repository-code: "https://github.com/gagolews/datafusion"
abstract: >
    A proper fusion of complex data is of interest to many researchers
    in diverse fields, including computational statistics, computational
    geometry, bioinformatics, machine learning, pattern recognition,
    quality management, engineering, statistics, finance, economics, etc.
    It plays a crucial role in: synthetic description of data processes
    or whole domains, creation of rule bases for approximate reasoning
    tasks, reaching consensus and selection of the optimal strategy in
    decision support systems, imputation of missing values, data
    deduplication and consolidation, record linkage across heterogeneous
    databases, and clustering. This open-access research monograph
    integrates the spread-out results from different domains using the
    methodology of the well-established classical aggregation framework,
    introduces researchers and practitioners to Aggregation 2.0,
    as well as points out the challenges and interesting directions
    for further research.
keywords:
  - data aggregation
  - data fusion
  - means
  - t-norms
  - spread measures
  - multidimensional data
  - strings
authors:
  - family-names: Gagolewski
    given-names: Marek
    orcid: "https://orcid.org/0000-0003-0637-6028"
    website: "https://www.gagolewski.com"
preferred-citation:
    type: book
    year: 2015
    title: "Data Fusion: Theory, Methods, and Applications"
    isbn: "978-83-63159-20-7"
    doi: 10.5281/zenodo.6960306
    publisher:
      name: "Institute of Computer Science, Polish Academy of Sciences"
      city: Warsaw
    authors:
      - family-names: Gagolewski
        given-names: Marek
        orcid: "https://orcid.org/0000-0003-0637-6028"
        website: "https://www.gagolewski.com"

GitHub Events

Total

Last Year

Issues and Pull Requests

Last synced: 11 months ago

All Time

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science