Search

Your search keyword '"Tsihrintzis, George A."' showing total 24 results
Start Over Author "Tsihrintzis, George A." Search Limiters Peer Reviewed Topic clustering

Refine your results

more »

more »

24 results on '"Tsihrintzis, George A."'

2. Investigating cluster validation metrics for optimal number of clusters determination.

Author

Karanikola, Aikaterini, Liapis, Charalampos M., Kotsiantis, Sotiris, Tsihrintzis, George A., Virvou, Maria, and Hatzilygeroudis, Ioannis

Subjects
PARALLEL algorithms, GALAXY clusters
Abstract

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term "optimal" is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

3. Artificial Immune System-Based Learning Style Stereotypes.

Author

Sotiropoulos, Dionisios N., Alepis, Efthimios, Kabassi, Katerina, Virvou, Maria K., Tsihrintzis, George A., and Sakkopoulos, Evangelos

Subjects
COGNITIVE styles, SYNTHETIC antibodies, K-means clustering, STEREOTYPES, PATTERN perception, GENDER stereotypes, REINFORCEMENT learning
Abstract

This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

6. Clustering of Building Stock

Author

Prina, Matteo Giacomo, Oberegger, Ulrich Filippi, Antonucci, Daniele, Ma, Yixiao, Shamsi, Mohammad Haris, Sharifi, Mohsen, Tsihrintzis, George A., Series Editor, Virvou, Maria, Series Editor, Jain, Lakhmi C., Series Editor, Doukas, Haris, editor, Marinakis, Vangelis, editor, and Sarmas, Elissaios, editor

Published
2024
Full Text
View/download PDF

8. Machine Learning Approaches and Particle Swarm Optimization Based Clustering for the Human Monkeypox Viruses: A Study

Author

Mandal, Akshaya Kumar, Sarma, Pankaj Kumar Deva, Dehuri, Satchidananda, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Panda, Mrutyunjaya, editor, Dehuri, Satchidananda, editor, Patra, Manas Ranjan, editor, Behera, Prafulla Kumar, editor, Tsihrintzis, George A., editor, Cho, Sung-Bae, editor, and Coello Coello, Carlos A., editor

Published
2022
Full Text
View/download PDF

9. Energy Efficient Adaptive Mobile Wireless Sensor Network in Smart Monitoring Applications

Author

Mohapatra, Seli, Behera, Prafulla Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Panda, Mrutyunjaya, editor, Dehuri, Satchidananda, editor, Patra, Manas Ranjan, editor, Behera, Prafulla Kumar, editor, Tsihrintzis, George A., editor, Cho, Sung-Bae, editor, and Coello Coello, Carlos A., editor

Published
2022
Full Text
View/download PDF

10. Simple Linear Iterative Clustering with Efficiency

Author

Liao, Nannan, Liu, Hui, Li, Cheng, Ren, Xia, Guo, Baolong, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Chu, Shu-Chuan, editor, Chen, Shi-Huang, editor, Meng, Zhenyu, editor, Ryu, Keun Ho, editor, and Tsihrintzis, George A., editor

Published
2022
Full Text
View/download PDF

18. A Study of Different Techniques in Educational Data Mining

Author

Anjum, Nadia, Badugu, Srinivasu, Tsihrintzis, George A., Series Editor, Virvou, Maria, Series Editor, Jain, Lakhmi C., Series Editor, Satapathy, Suresh Chandra, editor, Raju, K. Srujan, editor, Shyamala, K., editor, Krishna, D. Rama, editor, and Favorskaya, Margarita N., editor

Published
2020
Full Text
View/download PDF

22. Αλγόριθμοι συνεργατικού φιλτραρίσματος, Μέθοδοι και τεχνικές

Author

Moustaka, Aikaterini, Τσιχριντζής, Γεώργιος, Tsihrintzis, George, Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Πληροφορικής, and Προηγμένα Συστήματα Πληροφορικής

Subjects
Συνεργατικό φιλτράρισμα, Collaborative filtering, Machine learning, Clustering
Abstract

Στην παρούσα μεταπτυχιακή διατριβή, θα μελετηθούν οι αλγόριθμοι συνεργατικού φιλτραρίσματος, οι οποίοι χρησιμοποιούνται για την παραγωγή προσωποποιημένων αποτελεσμάτων ως προς τους χρήστες και πώς αυτό επηρεάζει την καθημερινότητά μας., The purpose of this project is to study the algorithms that are used to produce more personalized results, for the users and how they are affecting our world.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results