H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 75 Citations 37,140 228 World Ranking 581 National Ranking 2

Overview

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.

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.

Top Publications

Principles of data mining

David J. Hand;Heikki Mannila;Padhraic Smyth.
MIT Press Books (2001)

4987 Citations

Fast discovery of association rules

Rakesh Agrawal;Heikki Mannila;Ramakrishnan Srikant;Hannu Toivonen.
knowledge discovery and data mining (1996)

3650 Citations

Discovery of Frequent Episodes in Event Sequences

Heikki Mannila;Hannu Toivonen;A. Inkeri Verkamo.
Data Mining and Knowledge Discovery (1997)

2981 Citations

Efficient algorithms for discovering association rules

Heikki Mannila;Hannu Toivonen;A. Inkeri Verkamo.
knowledge discovery and data mining (1994)

1190 Citations

Discovering frequent episodes in sequences extended abstract

Heikki Mannila;Hannu Toivonen;A. Inkeri Verkamo.
knowledge discovery and data mining (1995)

1107 Citations

Finding interesting rules from large sets of discovered association rules

Mika Klemettinen;Heikki Mannila;Pirjo Ronkainen;Hannu Toivonen.
conference on information and knowledge management (1994)

1101 Citations

Random projection in dimensionality reduction: applications to image and text data

Ella Bingham;Heikki Mannila.
knowledge discovery and data mining (2001)

985 Citations

Levelwise Search and Borders of Theories in KnowledgeDiscovery

Heikki Mannila;Hannu Toivonen.
Data Mining and Knowledge Discovery (1997)

963 Citations

Rule discovery from time series

Gautam Das;King-Ip Lin;Heikki Mannila;Gopal Renganathan.
knowledge discovery and data mining (1998)

957 Citations

Discovering Frequent Episodes in Sequences.

Heikki Mannila;Hannu Toivonen;A. Inkeri Verkamo.
knowledge discovery and data mining (1995)

925 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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