2022 - Research.com Computer Science in Cyprus Leader Award
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Recommender system, Information retrieval and Cluster analysis. The Data mining study combines topics in areas such as Object and Path. Yannis Manolopoulos interconnects Machine learning, Metric and Pattern recognition in the investigation of issues within Artificial intelligence.
The concepts of his Recommender system study are interwoven with issues in Accuracy improvement, Credibility, Higher-order singular value decomposition and Contrast. His Information retrieval research incorporates elements of Access method, Metadata and Dimensionality reduction. His Search engine indexing study deals with Time series database intersecting with Database.
Data mining, Algorithm, Information retrieval, Search engine indexing and Theoretical computer science are his primary areas of study. His Data mining study incorporates themes from Access method, Database, Recommender system and Cluster analysis, Artificial intelligence. Specifically, his work in Recommender system is concerned with the study of Collaborative filtering.
His Artificial intelligence research includes elements of Machine learning and Pattern recognition. A large part of his Algorithm studies is devoted to Quadtree. He has researched Search engine indexing in several fields, including Scalability and Data structure.
Yannis Manolopoulos mostly deals with Data science, Data mining, Information retrieval, Skyline and Recommender system. The various areas that Yannis Manolopoulos examines in his Data science study include Scientometrics, Categorization, Data analysis and Big data. His Data mining research is multidisciplinary, incorporating elements of Context, Access method, Spatial database, Spatial query and Collaborative filtering.
Yannis Manolopoulos works on Information retrieval which deals in particular with Search engine indexing. His work carried out in the field of Search engine indexing brings together such families of science as Tree based and Data structure. His studies deal with areas such as Artificial intelligence and Social network as well as Recommender system.
Yannis Manolopoulos mainly focuses on Data mining, Recommender system, Information and Computer Science, Information retrieval and Big data. Yannis Manolopoulos has included themes like Scalability, Theoretical computer science, Social network, Artificial intelligence and Join in his Data mining study. His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Recommender system study also includes
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Fast subsequence matching in time-series databases
Christos Faloutsos;M. Ranganathan;Yannis Manolopoulos.
international conference on management of data (1994)
Data Mining techniques for the detection of fraudulent financial statements
Efstathios Kirkos;Charalambos Spathis;Yannis Manolopoulos.
Expert Systems With Applications (2007)
Generalized Hirsch h-index for disclosing latent facts in citation networks
Antonis Sidiropoulos;Dimitrios Katsaros;Yannis Manolopoulos.
Scientometrics (2007)
R-Trees: Theory and Applications
Yannis Manolopoulos;Alexandros Nanopoulos;Apostolos N. Papadopoulos;Yannis Theodoridis.
(2005)
A data mining approach for location prediction in mobile environments
Gökhan Yavas;Dimitrios Katsaros;Özgür Ulusoy;Yannis Manolopoulos.
data and knowledge engineering (2005)
Closest pair queries in spatial databases
Antonio Corral;Yannis Manolopoulos;Yannis Theodoridis;Michael Vassilakopoulos.
international conference on management of data (2000)
Tag recommendations based on tensor dimensionality reduction
Panagiotis Symeonidis;Alexandros Nanopoulos;Yannis Manolopoulos.
conference on recommender systems (2008)
Feature-based classification of time-series data
Alex Nanopoulos;Rob Alcock;Yannis Manolopoulos.
Information processing and technology (2001)
A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis
P. Symeonidis;A. Nanopoulos;Y. Manolopoulos.
IEEE Transactions on Knowledge and Data Engineering (2010)
C2P: Clustering based on Closest Pairs
Alexandros Nanopoulos;Yannis Theodoridis;Yannis Manolopoulos.
very large data bases (2001)
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