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Algorithms
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New Algorithms for Visual Data Mining
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New Algorithms for Visual Data Mining

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 12692

Special Issue Editor

Dr. Rosane Minghim

E-Mail Website
Guest Editor
School of Computer Science and Information Technology, University College Cork, Western Road, T12 CY82 Cork, Ireland
Interests: information visualization; visual data mining; visual analytics; data science

Special Issue Information

Dear Colleagues,

In current times, scientific, industrial and societal developments rely heavily on data collection and understanding. Computational approaches are paramount to support such activities and the expectation is that the demand for reliable approaches to working with complex data will continue to increase.

Challenges in Data Science and Analytics have highlighted many circumstances where Data Mining, Machine Learning and Statistics methods need to be paired with strong user engagement to support exploratory data analysis as well as illustrative, demonstrative and data story telling tasks.  The fields of Data Visualization and Visual Analytics have thrived in this scenario, in the effort to provide support for understanding and explaining data and to support building and applying data models in a very extensive variety of applications.

This Special Issue calls for novel contributions in the development of algorithms and techniques that combine Data Mining (DM) and Machine Learning (ML) algorithms and strategies with Visual Layouts and interaction to support user engagement in any part of the processes of  Data Science.

Papers on novel approaches and algorithms are welcome in subjects related to Visual Data Mining and applications.  Target subjects include, but are not limited to the following:

  • Machine Learning approaches adapted to user engagement.
  • User-centered machine learning
  • Visual Feedback in data mining and data analysis
  • Visual Clustering and Cluster Analysis
  • Visual Classification
  • Visual Regression
  • Visual approaches to data exploratory analysis supported by ML and DM algorithms.
  • Visual learning approaches for attribute analysis and selection.
  • Visual learning approaches to data labeling and annotation.
  • Visual and ML approaches to data retrieval.
  • ML and DM algorithms in support to Data Visualization
  • Visual strategies for interpretation of Machine Learning methods.
  • Visual Mappings for interpretation of multi-dimensional data, dimension reduction strategies and embeddings.
  • Combined Point-based and Attribute-based Visualizations.
  • Applications of Visual Data Mining in science, technology and industry, such as text and image mining, drug development, disease understanding, diagnosis and prognosis, physics, chemistry, biology and other scientific fields, social networks, news and fake news, monitoring of natural environments, etc..
  • Design issues for Visual Data Mining Tools.
  • Time related and Incremental Visual Learning.

Dr. Rosane Minghim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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28 pages, 2819 KiB  
Article
A Visual Mining Approach to Improved Multiple- Instance Learning
by Sonia Castelo, Moacir Ponti and Rosane Minghim
Algorithms 2021, 14(12), 344; https://0-doi-org.brum.beds.ac.uk/10.3390/a14120344 - 27 Nov 2021
Viewed by 2148
Abstract
Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, [...] Read more.
Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming an MIL problem into standard supervised learning. Visualization can be a useful tool to assess learning scenarios by incorporating the users’ knowledge into the classification process. Considering that multiple-instance learning is a paradigm that cannot be handled by current visualization techniques, we propose a multiscale tree-based visualization called MILTree to support MIL problems. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing users to understand the MIL datasets in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help users improve the model even further. Our methods can handle both binary and multiclass scenarios. In our experiments, SVM was used to build the classifiers. With support of the MILTree layout, the initial classification model was updated by changing the training set, which is composed of the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can support exploring and improving models in MIL scenarios and that our instance selection methods outperform the currently available alternatives in most cases. Full article
(This article belongs to the Special Issue New Algorithms for Visual Data Mining)
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15 pages, 1204 KiB  
Article
An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty
by Sharif Noor Zisad, Etu Chowdhury, Mohammad Shahadat Hossain, Raihan Ul Islam and Karl Andersson
Algorithms 2021, 14(7), 213; https://0-doi-org.brum.beds.ac.uk/10.3390/a14070213 - 15 Jul 2021
Cited by 19 | Viewed by 3398
Abstract
Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions [...] Read more.
Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual. Full article
(This article belongs to the Special Issue New Algorithms for Visual Data Mining)
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Review

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35 pages, 3929 KiB  
Review
Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
by Anestis Kousis and Christos Tjortjis
Algorithms 2021, 14(8), 242; https://0-doi-org.brum.beds.ac.uk/10.3390/a14080242 - 17 Aug 2021
Cited by 19 | Viewed by 6172
Abstract
Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used [...] Read more.
Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field. Full article
(This article belongs to the Special Issue New Algorithms for Visual Data Mining)
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