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Algorithms
Special Issues
Advanced Data Mining: Algorithms and Applications
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Advanced Data Mining: Algorithms and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 8506

Special Issue Editors

Dr. Youcef Djenouri

E-Mail Website
Guest Editor
Department of Computer and Information Science, Norges teknisk-naturvitenskapelige universitet (NTNU), Trondheim, Norway
Interests: data mining; parallel computing; optimization methods, smart city applications
Prof. Dr. Jerry Chun-Wei Lin

E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: AI and machine learning; data analytics; optimization; soft computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research of advanced data mining algorithms to this Special Issue. We are looking for new and innovative approaches for solving data mining problems, including clustering, pattern mining, association rules, classification, outlier detection, and feature selections. We also expect to show the usefulness of advanced data mining on traditional and emerging applications. Potential topics include but are not limited to information retrieval, process mining, intelligent transportation, smart building, and smart healthcare. We solicite approaches dealing with big databases by investigating both emerging high performance computing technologies and optimization algorithms.

Dr. Youcef Djenouri
Dr. Jerry Chun-Wei Lin
Guest Editors

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.

Keywords

  1. Fundamental data mining
    • Clustering
    • Pattern mining
    • Association rules
    • Classification
    • Outlier detection
    • Feature selection
    • Exact and approximate-based approaches
    • Evoluationary and swarm intelligence approaches
  2. Data mining applications
    • Information retrieval
    • Process mining
    • Intelligent transportation
    • Smart building
    • Smart healthcare

Published Papers (2 papers)

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Research

27 pages, 5839 KiB  
Article
Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques
by Abdul Karim, Azhari Azhari, Samir Brahim Belhaouri, Ali Adil Qureshi and Maqsood Ahmad
Algorithms 2020, 13(8), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/a13080202 - 18 Aug 2020
Cited by 4 | Viewed by 4668
Abstract
Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google [...] Read more.
Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google Play Store, which is one of the most popular Internet application stores, users are encouraged to download thousands of applications and various types of software. In this research study, we have scraped thousands of user reviews and the ratings of different applications. We scraped 148 application reviews from 14 different categories. A total of 506,259 reviews were accumulated and assessed. Based on the semantics of reviews of the applications, the results of the reviews were classified negative, positive or neutral. In this research, different machine-learning algorithms such as logistic regression, random forest and naïve Bayes were tuned and tested. We also evaluated the outcome of term frequency (TF) and inverse document frequency (IDF), measured different parameters such as accuracy, precision, recall and F1 score (F1) and present the results in the form of a bar graph. In conclusion, we compared the outcome of each algorithm and found that logistic regression is one of the best algorithms for the review-analysis of the Google Play Store from an accuracy perspective. Furthermore, we were able to prove and demonstrate that logistic regression is better in terms of speed, rate of accuracy, recall and F1 perspective. This conclusion was achieved after preprocessing a number of data values from these data sets. Full article
(This article belongs to the Special Issue Advanced Data Mining: Algorithms and Applications)
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12 pages, 3728 KiB  
Article
Local-Topology-Based Scaling for Distance Preserving Dimension Reduction Method to Improve Classification of Biomedical Data-Sets
by Karaj Khosla, Indra Prakash Jha, Ajit Kumar and Vibhor Kumar
Algorithms 2020, 13(8), 192; https://0-doi-org.brum.beds.ac.uk/10.3390/a13080192 - 10 Aug 2020
Cited by 3 | Viewed by 3062
Abstract
Dimension reduction is often used for several procedures of analysis of high dimensional biomedical data-sets such as classification or outlier detection. To improve the performance of such data-mining steps, preserving both distance information and local topology among data-points could be more useful than [...] Read more.
Dimension reduction is often used for several procedures of analysis of high dimensional biomedical data-sets such as classification or outlier detection. To improve the performance of such data-mining steps, preserving both distance information and local topology among data-points could be more useful than giving priority to visualization in low dimension. Therefore, we introduce topology-preserving distance scaling (TPDS) to augment a dimension reduction method meant to reproduce distance information in a higher dimension. Our approach involves distance inflation to preserve local topology to avoid collapse during distance preservation-based optimization. Applying TPDS on diverse biomedical data-sets revealed that besides providing better visualization than typical distance preserving methods, TPDS leads to better classification of data points in reduced dimension. For data-sets with outliers, the approach of TPDS also proves to be useful, even for purely distance-preserving method for achieving better convergence. Full article
(This article belongs to the Special Issue Advanced Data Mining: Algorithms and Applications)
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