What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Mathematical optimization, Simulated annealing, Heuristics, Heuristic and Operations research.
His Mathematical optimization research is multidisciplinary, incorporating elements of Customer lifetime value, Algorithm, Homogeneity and Market segmentation.
The study incorporates disciplines such as Complete-linkage clustering, Data mining and Feature selection in addition to Algorithm.
His biological study spans a wide range of topics, including Uniform distribution, Combinatorial optimization and Permutation.
His studies in Heuristics integrate themes in fields like Linear programming and Scheduling.
His Operations research research focuses on Operations management and how it connects with Time horizon and Branch and bound.
His most cited work include:
- Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques (192 citations)
- Service Sweethearting: Its Antecedents and Customer Consequences (132 citations)
- Note: A local-search heuristic for large set-covering problems (128 citations)
What are the main themes of his work throughout his whole career to date?
Michael J. Brusco spends much of his time researching Mathematical optimization, Cluster analysis, Algorithm, Heuristics and Heuristic.
Mathematical optimization is frequently linked to Combinatorial data analysis in his study.
His Cluster analysis research includes elements of Data mining and Partition.
His Algorithm research is multidisciplinary, incorporating perspectives in Feature selection, Row and column spaces, k-means clustering and Symmetric matrix.
His study explores the link between Heuristics and topics such as Operations research that cross with problems in Operations management, Time horizon and Service system.
His Integer programming course of study focuses on Scheduling and Linear programming.
He most often published in these fields:
- Mathematical optimization (38.10%)
- Cluster analysis (28.57%)
- Algorithm (20.83%)
What were the highlights of his more recent work (between 2017-2021)?
- Statistics (10.12%)
- Cluster analysis (28.57%)
- Algorithm (20.83%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Statistics, Cluster analysis, Algorithm, Data mining and Heuristics.
His Statistics study incorporates themes from Customer satisfaction and Residual.
His Algorithm research includes themes of Test, Test theory, Feature and Heuristic.
His work deals with themes such as Cohen's kappa, Data point and Categorical variable, which intersect with Data mining.
Mathematical optimization covers he research in Heuristics.
When carried out as part of a general Mathematical optimization research project, his work on Scheduling and Integer programming is frequently linked to work in Augmentation procedure, Cyclic shift and Shift length, therefore connecting diverse disciplines of study.
Between 2017 and 2021, his most popular works were:
- Detecting Clusters/Communities in Social Networks. (12 citations)
- Supply Chain and Marketing Integration: Tension in Frontline Social Networks (11 citations)
- Partitioning signed networks using relocation heuristics, tabu search, and variable neighborhood search (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His main research concerns Algorithm, Statistics, Pairwise comparison, Heuristic and Mode.
He carries out multidisciplinary research, doing studies in Algorithm and Ising model.
His study brings together the fields of Logical matrix and Statistics.
His work carried out in the field of Pairwise comparison brings together such families of science as Similarity and Sample.
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.
