What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Cluster analysis, Search engine indexing and Pattern recognition.
Dimitrios Gunopulos integrates several fields in his works, including Data mining and Constraint.
Representation is closely connected to Machine learning in his research, which is encompassed under the umbrella topic of Artificial intelligence.
In Cluster analysis, Dimitrios Gunopulos works on issues like Algorithm, which are connected to Temporal database.
In general Pattern recognition study, his work on Dimensionality reduction and k-nearest neighbors algorithm often relates to the realm of Similarity, thereby connecting several areas of interest.
Dimitrios Gunopulos focuses mostly in the field of CURE data clustering algorithm, narrowing it down to matters related to Clustering high-dimensional data and, in some cases, Fuzzy clustering and Curse of dimensionality.
His most cited work include:
- Automatic subspace clustering of high dimensional data for data mining applications (2306 citations)
- Discovering similar multidimensional trajectories (1127 citations)
- Mining Process Models from Workflow Logs (707 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Artificial intelligence, Search engine indexing, Wireless sensor network and Cluster analysis.
When carried out as part of a general Data mining research project, his work on Temporal database is frequently linked to work in Context, therefore connecting diverse disciplines of study.
Dimitrios Gunopulos works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Curse of dimensionality, as a part of the same area of interest.
His work carried out in the field of Search engine indexing brings together such families of science as Object, Theoretical computer science and Longest common subsequence problem.
His Wireless sensor network research also works with subjects such as
- Distributed computing and related Scalability,
- Real-time computing and related Global Positioning System.
All of his Cluster analysis and Correlation clustering, CURE data clustering algorithm, Canopy clustering algorithm, Clustering high-dimensional data and Fuzzy clustering investigations are sub-components of the entire Cluster analysis study.
He most often published in these fields:
- Data mining (36.27%)
- Artificial intelligence (16.27%)
- Search engine indexing (12.88%)
What were the highlights of his more recent work (between 2015-2021)?
- Data mining (36.27%)
- Global Positioning System (3.39%)
- Data science (8.81%)
In recent papers he was focusing on the following fields of study:
Dimitrios Gunopulos mostly deals with Data mining, Global Positioning System, Data science, Trajectory and Social network.
He combines subjects such as Point cloud, Competitor analysis and Cluster analysis with his study of Data mining.
His work on Gps data is typically connected to Location data, Sequence and City scale as part of general Global Positioning System study, connecting several disciplines of science.
His Data science research is multidisciplinary, incorporating elements of Social media and Data management.
The Social network study combines topics in areas such as Anomaly detection and Mathematical optimization.
Within one scientific family, Dimitrios Gunopulos focuses on topics pertaining to Map matching under Location-based service, and may sometimes address concerns connected to Artificial intelligence.
Between 2015 and 2021, his most popular works were:
- City-Scale Map Creation and Updating using GPS Collections (39 citations)
- Detecting Events in Online Social Networks: Definitions, Trends and Challenges (34 citations)
- Intelligent Urban Data Monitoring for Smart Cities (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
His primary areas of study are Data mining, Competitor analysis, Data science, Global Positioning System and Real-time computing.
His study in Data mining is interdisciplinary in nature, drawing from both Point cloud, Graph and Web mining.
His studies in Competitor analysis integrate themes in fields like Computer security, Misinformation and Optimization problem.
His Data science research includes themes of Field, Open research, Social media and Data management.
His study explores the link between Global Positioning System and topics such as Path that cross with problems in Baseline, Taxis and Artificial intelligence.
His work deals with themes such as Dynamic load balancing, Load balancing, Distributed computing and State management, which intersect with Real-time computing.
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