Change search
Link to record
Permanent link

Direct link
BETA
Vishnu Manasa, Devagiri
Publications (2 of 2) Show all publications
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Leslie Chan, Pierre Mounier (Ed.), Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
Open this publication in new window or tab >>An Expertise Recommender System based on Data from an Institutional Repository (DiVA)
Show others...
2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press, 2019
Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-18095 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Note

open access

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-19Bibliographically approved
Boeva, V., Angelova, M., Angelova, M., Vishnu Manasa, D. & Tsiporkova, E. (2019). Bipartite Split-Merge Evolutionary Clustering. In: Lect. Notes Comput. Sci.: . Paper presented at 11th International Conference on Agents and Artificial Intelligence, ICAART; Prague; Czech Republic; 19 February 2019 through 21 February (pp. 204-223). Springer
Open this publication in new window or tab >>Bipartite Split-Merge Evolutionary Clustering
Show others...
2019 (English)In: Lect. Notes Comput. Sci., Springer , 2019, p. 204-223Conference paper, Published paper (Refereed)
Abstract [en]

We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge). © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Bipartite clustering, Data mining, Dynamic clustering, Evolutionary clustering, Split-merge framework, Unsupervised learning, Artificial intelligence, Bipartite correlation clustering, Clustering solutions, Clustering techniques, State-of-the-art algorithms, Cluster analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19127 (URN)10.1007/978-3-030-37494-5_11 (DOI)2-s2.0-85077496461 (Scopus ID)9783030374938 (ISBN)
Conference
11th International Conference on Agents and Artificial Intelligence, ICAART; Prague; Czech Republic; 19 February 2019 through 21 February
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-23Bibliographically approved
Organisations

Search in DiVA

Show all publications