What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Machine learning, Case-based reasoning, Data mining and Pattern recognition.
His research brings together the fields of Technical report and Artificial intelligence.
Many of his research projects under Machine learning are closely connected to Context with Context, tying the diverse disciplines of science together.
His Case-based reasoning study integrates concerns from other disciplines, such as Similarity, Lazy learning, Software design and Adaptation.
The study incorporates disciplines such as Key, Set, Projection and Document clustering in addition to Data mining.
His work in the fields of Pattern recognition, such as Tree kernel and Kernel principal component analysis, intersects with other areas such as Matrix decomposition and Non-negative matrix factorization.
His most cited work include:
- Tracking the Evolution of Communities in Dynamic Social Networks (374 citations)
- Diversity in search strategies for ensemble feature selection (264 citations)
- Practical solutions to the problem of diagonal dominance in kernel document clustering (244 citations)
What are the main themes of his work throughout his whole career to date?
Pádraig Cunningham focuses on Artificial intelligence, Machine learning, Data mining, Case-based reasoning and World Wide Web.
His studies deal with areas such as Concept drift and Pattern recognition as well as Artificial intelligence.
His study in Machine learning is interdisciplinary in nature, drawing from both Training set and Task.
His Data mining research is multidisciplinary, incorporating perspectives in Set, DNA microarray, Information retrieval and Cluster analysis.
He interconnects Reasoning system and Model-based reasoning in the investigation of issues within Case-based reasoning.
His is involved in several facets of World Wide Web study, as is seen by his studies on Social media, Recommender system and Crowdsourcing.
He most often published in these fields:
- Artificial intelligence (42.22%)
- Machine learning (24.44%)
- Data mining (20.00%)
What were the highlights of his more recent work (between 2012-2020)?
- Data science (8.89%)
- Artificial intelligence (42.22%)
- World Wide Web (13.02%)
In recent papers he was focusing on the following fields of study:
Pádraig Cunningham mostly deals with Data science, Artificial intelligence, World Wide Web, Social media and Data mining.
Pádraig Cunningham has researched Data science in several fields, including Task and Plan.
Pádraig Cunningham has included themes like Natural language processing, Machine learning and Pattern recognition in his Artificial intelligence study.
His study brings together the fields of Training set and Machine learning.
His World Wide Web research incorporates themes from Multimedia and Stream processing.
His research integrates issues of Social relation and Dynamic network analysis in his study of Data mining.
Between 2012 and 2020, his most popular works were:
- An analysis of the coherence of descriptors in topic modeling (116 citations)
- How Many Topics? Stability Analysis for Topic Models (67 citations)
- Producing a unified graph representation from multiple social network views (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Social media, Topic model, Artificial intelligence, Recommender system and Information retrieval.
His studies in Social media integrate themes in fields like Social network analysis, Social network and Marketing.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Viewpoints and Natural language processing.
His research in Machine learning intersects with topics in Coherence and Generality.
His research investigates the connection between Recommender system and topics such as Sports analytics that intersect with problems in Multimedia, Quality and Operations research.
His Information retrieval research is multidisciplinary, relying on both Stability, Thematic structure, Model selection and Text corpus.
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