What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Martin Ester focuses on Data mining, Cluster analysis, Database, CURE data clustering algorithm and Spatial database.
His studies deal with areas such as Object, Subspace topology and Artificial intelligence as well as Data mining.
His Cluster analysis study focuses mostly on Determining the number of clusters in a data set and Clustering high-dimensional data.
His Database study frequently draws connections between adjacent fields such as Algorithm.
Data stream clustering, DBSCAN, SUBCLU and OPTICS algorithm are the core of his CURE data clustering algorithm study.
Martin Ester usually deals with DBSCAN and limits it to topics linked to Single-linkage clustering and FLAME clustering and Complete-linkage clustering.
His most cited work include:
- A density-based algorithm for discovering clusters in large spatial Databases with Noise (12123 citations)
- PSORTb 3.0 (1340 citations)
- A matrix factorization technique with trust propagation for recommendation in social networks (1026 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Recommender system.
His research in Data mining intersects with topics in Algorithm, Spatial database and Database.
His work on Artificial neural network, Probabilistic logic and Deep neural networks as part of general Artificial intelligence study is frequently linked to Set, bridging the gap between disciplines.
His work in Correlation clustering, CURE data clustering algorithm, Single-linkage clustering, Fuzzy clustering and Determining the number of clusters in a data set are all subfields of Cluster analysis research.
Data stream clustering, SUBCLU, OPTICS algorithm and DBSCAN are the primary areas of interest in his CURE data clustering algorithm study.
His Recommender system research is multidisciplinary, incorporating elements of Set and Social network.
He most often published in these fields:
- Data mining (37.33%)
- Artificial intelligence (33.64%)
- Machine learning (24.42%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (33.64%)
- Machine learning (24.42%)
- Computational biology (8.29%)
In recent papers he was focusing on the following fields of study:
Martin Ester spends much of his time researching Artificial intelligence, Machine learning, Computational biology, Artificial neural network and Gene expression.
His study on Deep neural networks, Cluster analysis and Deep learning is often connected to Noise as part of broader study in Artificial intelligence.
His research is interdisciplinary, bridging the disciplines of Probabilistic logic and Cluster analysis.
His Machine learning research includes elements of Domain, Precision medicine, Representation and Bayesian probability.
His studies in Bayesian probability integrate themes in fields like Node and Recommender system.
His Computational biology research incorporates themes from Feature extraction, Metastasis, Prostate cancer and Robustness.
Between 2017 and 2021, his most popular works were:
- ANRL: Attributed Network Representation Learning via Deep Neural Networks (79 citations)
- MOLI: multi-omics late integration with deep neural networks for drug response prediction (48 citations)
- MOLI: multi-omics late integration with deep neural networks for drug response prediction (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Machine learning, Artificial neural network, Inference and Deep neural networks.
His Artificial intelligence study integrates concerns from other disciplines, such as In vitro and Omics.
His work deals with themes such as Domain, Germline mutation, Pharmacogenomics, Gene expression and Sample, which intersect with Machine learning.
His Artificial neural network research integrates issues from Transfer of learning, Outcome, Test data and Adaptation.
The study incorporates disciplines such as Precision medicine, Representation and In vivo in addition to Deep neural networks.
His work on Spectral clustering as part of general Cluster analysis study is frequently linked to Computational creativity, therefore connecting diverse disciplines of science.
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.
