What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
His primary areas of investigation include Cluster analysis, Web mining, Personalization, World Wide Web and Artificial intelligence.
His studies in Cluster analysis integrate themes in fields like Algorithm, Data mining and Fuzzy set, Fuzzy logic.
His work carried out in the field of Data mining brings together such families of science as Field and Outlier.
His work deals with themes such as Web intelligence and Data Web, which intersect with Web mining.
His Personalization study combines topics in areas such as Recommender system, Information retrieval, Collaborative filtering, Web server and User profile.
His research integrates issues of Machine learning, Scalability and Pattern recognition in his study of Artificial intelligence.
His most cited work include:
- Low-complexity fuzzy relational clustering algorithms for Web mining (311 citations)
- Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II (206 citations)
- Mining search engine query logs for query recommendation (206 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Cluster analysis, Data mining, World Wide Web and Web mining.
His Artificial intelligence research incorporates elements of Machine learning, Computer vision and Pattern recognition.
His work in Cluster analysis tackles topics such as Algorithm which are related to areas like Determining the number of clusters in a data set and Pattern recognition.
When carried out as part of a general Data mining research project, his work on Data stream mining is frequently linked to work in Clickstream, therefore connecting diverse disciplines of study.
The concepts of his Web mining study are interwoven with issues in Personalization, Web analytics, Knowledge extraction, Data science and Data Web.
The Personalization study which covers Recommender system that intersects with Social media.
He most often published in these fields:
- Artificial intelligence (34.50%)
- Cluster analysis (33.00%)
- Data mining (27.50%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (34.50%)
- Recommender system (21.00%)
- Machine learning (16.50%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Recommender system, Machine learning, Collaborative filtering and Big data.
Olfa Nasraoui interconnects Iterated function, Pattern recognition and Personalization in the investigation of issues within Artificial intelligence.
Olfa Nasraoui has included themes like Social media, Educational data mining, Data mining and Open educational resources in his Recommender system study.
The various areas that Olfa Nasraoui examines in his Data mining study include Subnetwork and Information retrieval.
The study incorporates disciplines such as Data set and Taxonomy in addition to Machine learning.
The Big data study combines topics in areas such as World Wide Web, Learning object and Semantic Web.
Between 2014 and 2021, his most popular works were:
- Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints (123 citations)
- Using Explainability for Constrained Matrix Factorization (41 citations)
- Explainable Matrix Factorization for Collaborative Filtering (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- World Wide Web
The scientist’s investigation covers issues in Artificial intelligence, Recommender system, Machine learning, Data mining and Collaborative filtering.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Iterated function and Transparency.
His research in Recommender system intersects with topics in Learning object, Learning analytics, Learning Management, Educational data mining and Open educational resources.
His Machine learning research incorporates themes from Special case and Taxonomy.
Olfa Nasraoui works mostly in the field of Data mining, limiting it down to topics relating to Interpretability and, in certain cases, Boltzmann machine.
His Matrix decomposition research overlaps with other disciplines such as State, Information retrieval, Quality, Focus and Factorization.
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.
