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Algorithms
Special Issues
Effective Algorithms for Intelligent Data Mining and Knowledge Discovery
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Effective Algorithms for Intelligent Data Mining and Knowledge Discovery

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 5700

Special Issue Editors

Prof. Dr. Danilo Pelusi

E-Mail Website
Guest Editor
Department of Computer Science, University of Teramo, 64100 Teramo, Italy
Interests: fuzzy logic; machine learning; evolutionary algorithms; computational intelligence; information theory
Special Issues, Collections and Topics in MDPI journals
Dr. Asit Kumar Das

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, West Bengal, India
Interests: machine learning; soft computing; natural language processing; artificial intelligence; pattern recognition; graph algorithms; information retrieval; decision support system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various types of structured and unstructured data are continuously generated throughout the glove. Some data are quite useful in decision making, whereas others are irrelevant, inconsistent, or inaccurate. The process of building valid, unique, fascinating, and potentially useful knowledge from large amounts of data is known as data mining and knowledge discovery. It entails assessing and perhaps even interpreting patterns in order to determine what qualifies as knowledge. It is focused on the creation and application of efficient algorithms capable of analyzing data and extracting meaningful information. To attain the needed information, some commonly used technologies include deep neural networks, natural language processing, soft computing, and artificial intelligence. Mathematical analysis is used to express representations, model, and synthesize empirical data or observations from the real world. One of the most useful steps in developing a decision support system is data pretreatment and feature extraction, for which numerous intelligence algorithms based on natural language processing, image processing, and various soft computing techniques play essential roles.

As a result, the goal of this Special Issue is to considerably advance the state of the art in this field by offering a thorough examination and comparison of existing and newly developed works. We are pleased to announce that the MDPI journal Algorithms will publish a Special Issue titled “Effective Algorithms for Intelligent Data Mining and Knowledge Discovery”. This Special Issue focuses on the design of artificial intelligence-based algorithms for data mining and knowledge discovery, including clustering, classification, and summarization, using natural language processing, image, audio, and video processing, machine learning, and other related topics. We are looking for articles that deal with such approaches and present unique results relating to the theoretical analysis and understanding of such algorithms, as well as papers that indicate new improvements in the algorithms or deal with relevant applications. We urge authors from all over the world to submit their unpublished original works. In addition, we seek extensive surveys with thought-provoking questions to considerably improve the state of the art in this field. We are particularly interested in works that address the issues listed below, but we are open to any submissions that fit the Special Issue’s theme.

Dr. Danilo Pelusi
Dr. Asit Kumar Das
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

  •  natural language processing
  •  image processing
  •  data mining and knowledge discovery
  •  soft computing
  •  artificial intelligence
  •  machine learning

Published Papers (2 papers)

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Research

17 pages, 1134 KiB  
Article
Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology
by Nathalie Campos Macias, Wilhelm Düggelin, Yesim Ruf and Thomas Hanne
Algorithms 2022, 15(8), 272; https://doi.org/10.3390/a15080272 - 03 Aug 2022
Cited by 7 | Viewed by 3115
Abstract
Finding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a [...] Read more.
Finding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a technology recommender system by developing a prototype of such a system. It also analyzes how well the resulting prototype performs in regard to effectivity and efficiency. The research strategy based on design science research consists of four stages: (1) Awareness generation; (2) suggestion of a solution considering the information retrieval process; (3) development of an artefact in the form of a Python computer program; and (4) evaluation of the prototype within the scope of a comparative experiment. The evaluation yields that the prototype is highly efficient in retrieving basic and rather random extractive text summaries from websites that include the desired search terms. However, the effectivity, measured by the quality of results is unsatisfactory due to the aforementioned random arrangement of extracted sentences within the resulting summaries. It is found that natural language processing and web crawling are indeed suitable technologies for such a program whilst the use of additional technology/concepts would add significant value for a potential user. Several areas for incremental improvement of the prototype are identified. Full article
(This article belongs to the Special Issue Effective Algorithms for Intelligent Data Mining and Knowledge Discovery)
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17 pages, 2513 KiB  
Article
The Efficient Processing of Moving k-Farthest Neighbor Queries in Road Networks
by Hyung-Ju Cho
Algorithms 2022, 15(7), 223; https://doi.org/10.3390/a15070223 - 23 Jun 2022
Viewed by 1544
Abstract
Given a set of facilities F and a query point q, a k-farthest neighbor (kFN) query returns the k farthest facilities f1,f1,⋯,fk from q. This study considers the moving [...] Read more.
Given a set of facilities F and a query point q, a k-farthest neighbor (kFN) query returns the k farthest facilities f1,f1,⋯,fk from q. This study considers the moving k-farthest neighbor (MkFN) query that constantly retrieves the k facilities farthest from a moving query point q in a road network. The main challenge in processing MkFN queries in road networks is avoiding the repeated retrieval of candidate facilities as the query point arbitrarily moves along the road network. To this end, this study proposes a moving farthest search algorithm (MOFA) to compute valid segments for the query segment in which the query point is located. Each valid segment has the same k facilities farthest from the query locations in the valid segment. Therefore, MOFA retrieves candidate facilities only once for the query segment and computes valid segments using these candidate facilities, thereby avoiding the repeated retrieval of candidate facilities when the query point moves. An empirical study using real-world road networks demonstrates the superiority and scalability of MOFA compared to a conventional solution. Full article
(This article belongs to the Special Issue Effective Algorithms for Intelligent Data Mining and Knowledge Discovery)
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