What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Programming language
Data mining, Association rule learning, Artificial intelligence, Algorithm and Machine learning are his primary areas of study.
His Data mining research is multidisciplinary, relying on both Statistical hypothesis testing, Statistical model and Cluster analysis.
His Statistical model study combines topics from a wide range of disciplines, such as Metadata, Linkage disequilibrium, Missing data and Data pre-processing.
The Association rule learning study combines topics in areas such as Data stream mining and Set.
His studies link Natural language processing with Artificial intelligence.
His studies in Algorithm integrate themes in fields like Discrete mathematics, Functional dependency, Representation, Line segment and Scaling.
His most cited work include:
- Principles of data mining (2426 citations)
- Fast discovery of association rules (2238 citations)
- Discovery of Frequent Episodes in Event Sequences (1387 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Data mining, Artificial intelligence, Algorithm, Association rule learning and Theoretical computer science.
He works in the field of Data mining, focusing on Knowledge extraction in particular.
He combines subjects such as Information retrieval and Data science with his study of Knowledge extraction.
Heikki Mannila interconnects Machine learning, Pattern recognition and Natural language processing in the investigation of issues within Artificial intelligence.
Algorithm is frequently linked to Measure in his study.
His Set research incorporates elements of Time complexity, Sampling, Simple and Row.
He most often published in these fields:
- Data mining (38.58%)
- Artificial intelligence (20.06%)
- Algorithm (13.58%)
What were the highlights of his more recent work (between 2006-2020)?
- Data mining (38.58%)
- Cluster analysis (8.02%)
- Artificial intelligence (20.06%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data mining, Cluster analysis, Artificial intelligence, Algorithm and Row.
In the field of Data mining, his study on Null overlaps with subjects such as Randomization.
His Cluster analysis research integrates issues from Measure and Biogeography.
His Artificial intelligence study combines topics in areas such as Natural language processing, Machine learning and Pattern recognition.
His biological study deals with issues like Binary data, which deal with fields such as Computation.
His research in Row intersects with topics in Discrete mathematics, Matrix, Theoretical computer science and Markov chain Monte Carlo.
Between 2006 and 2020, his most popular works were:
- Clustering aggregation (562 citations)
- The Discrete Basis Problem (222 citations)
- Assessing data mining results via swap randomization (197 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Heikki Mannila mainly investigates Data mining, Cluster analysis, Sample, Ecology and Mammal.
Heikki Mannila undertakes multidisciplinary investigations into Data mining and Randomization techniques in his work.
He combines subjects such as Significance testing and Markov chain with his study of Cluster analysis.
His study looks at the relationship between Sample and topics such as Sampling, which overlap with Data set, Space, Database, Distribution and Probabilistic logic.
His Ecology study incorporates themes from Spatial distribution and Extinction.
Heikki Mannila has researched Set in several fields, including Association rule learning and Relational model, Relational database, Conjunctive query, Information retrieval.
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