What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Hiroshi Motoda mainly focuses on Data mining, Artificial intelligence, Feature selection, Machine learning and Mathematical optimization.
Hiroshi Motoda interconnects Application-specific integrated circuit, Model selection and Data science in the investigation of issues within Data mining.
His research in Artificial intelligence tackles topics such as Pattern recognition which are related to areas like Test set and Selection.
His studies deal with areas such as Selective sampling, Measure, Search algorithm and Dimensionality reduction as well as Feature selection.
His Mathematical optimization research is multidisciplinary, incorporating elements of Algorithm, Blocking, Degree and Social network.
His studies in Algorithm integrate themes in fields like Association rule learning and Supervised learning.
His most cited work include:
- Top 10 algorithms in data mining (3313 citations)
- Feature Selection for Knowledge Discovery and Data Mining (1590 citations)
- An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data (902 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Artificial intelligence, Machine learning, Graph and Algorithm are his primary areas of study.
His work deals with themes such as Set, Data science and Social network, which intersect with Data mining.
His work is dedicated to discovering how Artificial intelligence, Pattern recognition are connected with Graph based and other disciplines.
His Graph study combines topics from a wide range of disciplines, such as Computational complexity theory, Directed graph, Greedy algorithm, Pairwise comparison and Graph.
Many of his studies on Algorithm apply to Change detection as well.
He combines subjects such as Ripple-down rules, Knowledge base and Knowledge-based systems with his study of Knowledge extraction.
He most often published in these fields:
- Data mining (34.49%)
- Artificial intelligence (32.17%)
- Machine learning (18.26%)
What were the highlights of his more recent work (between 2013-2020)?
- Centrality (9.86%)
- Node (11.59%)
- Data mining (34.49%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Centrality, Node, Data mining, Network performance and Betweenness centrality.
His Centrality study also includes
- Identification which connect with Web mining, Feature extraction, Data stream mining, Association rule learning and Sentiment analysis,
- Resampling together with Standard error,
- Maximization and related Degree and Theoretical computer science,
- Spatial network which connect with Time constraint.
His Node study incorporates themes from Pruning, Mathematical optimization, Greedy algorithm, Computation and Complex network.
His biological study spans a wide range of topics, including Node, Set, Multinomial distribution, Information retrieval and Synthetic data.
His studies examine the connections between Probabilistic logic and genetics, as well as such issues in Statistical model, with regards to Data science.
His work carried out in the field of Katz centrality brings together such families of science as Machine learning and Artificial intelligence.
Between 2013 and 2020, his most popular works were:
- Super mediator - A new centrality measure of node importance for information diffusion over social network (42 citations)
- Accelerating Computation of Distance Based Centrality Measures for Spatial Networks (15 citations)
- Accelerating Computation of Distance Based Centrality Measures for Spatial Networks (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Hiroshi Motoda mainly focuses on Node, Centrality, Artificial intelligence, Machine learning and Betweenness centrality.
His Centrality research includes elements of Spatial network and Mathematical optimization, Maximization, Greedy algorithm.
His research in Artificial intelligence intersects with topics in Katz centrality, Order, Moment and Network controllability.
His Machine learning research includes themes of Baseline, Reliability, Social media mining and Mobile phone.
His Betweenness centrality research is multidisciplinary, relying on both Closeness and Data mining.
His work on Social network analysis expands to the thematically related Data mining.
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