What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Charu C. Aggarwal mostly deals with Data mining, Cluster analysis, Artificial intelligence, Data science and Data stream mining.
Her studies in Data mining integrate themes in fields like Data stream, Outlier and Data set.
Her Cluster analysis research integrates issues from Spatial analysis and Search engine indexing.
In her study, Classifier is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence.
Her Data science study incorporates themes from Variety, Field and World Wide Web, Social network.
Her Clustering high-dimensional data research includes themes of Nearest neighbor search and Curse of dimensionality.
Her most cited work include:
- A framework for clustering evolving data streams (1459 citations)
- On the Surprising Behavior of Distance Metrics in High Dimensional Spaces (1174 citations)
- On the design and quantification of privacy preserving data mining algorithms (905 citations)
What are the main themes of her work throughout her whole career to date?
Charu C. Aggarwal spends much of her time researching Data mining, Artificial intelligence, Cluster analysis, Data stream mining and Machine learning.
Her study explores the link between Data mining and topics such as Clustering high-dimensional data that cross with problems in Curse of dimensionality.
Her study in the field of Class, Subspace topology and Training set is also linked to topics like Process.
Cluster analysis is a component of her Fuzzy clustering, Correlation clustering, Data stream clustering, CURE data clustering algorithm and Canopy clustering algorithm studies.
Charu C. Aggarwal frequently studies issues relating to Data science and Data stream mining.
Her work carried out in the field of Data science brings together such families of science as Variety, Social media and Social network.
She most often published in these fields:
- Data mining (45.26%)
- Artificial intelligence (25.52%)
- Cluster analysis (15.89%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (25.52%)
- Data mining (45.26%)
- Machine learning (13.64%)
In recent papers she was focusing on the following fields of study:
Charu C. Aggarwal focuses on Artificial intelligence, Data mining, Machine learning, Theoretical computer science and Graph.
Her Artificial intelligence research focuses on Pattern recognition and how it connects with Data set.
Charu C. Aggarwal interconnects Feature extraction and Social network in the investigation of issues within Data mining.
Her research in Machine learning intersects with topics in Matrix decomposition and Optimization problem.
Her Theoretical computer science research includes elements of Graph classification and Link.
Her work investigates the relationship between Graph and topics such as Graph that intersect with problems in Embedding.
Between 2016 and 2021, her most popular works were:
- Outlier Detection with Autoencoder Ensembles. (141 citations)
- Signed network embedding in social media (130 citations)
- Neural Networks and Deep Learning (120 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Her scientific interests lie mostly in Artificial intelligence, Theoretical computer science, Data mining, Machine learning and Artificial neural network.
In her work, Data set is strongly intertwined with Pattern recognition, which is a subfield of Artificial intelligence.
Her work deals with themes such as Embedding, Network model, Manifold, Graph and Social media, which intersect with Theoretical computer science.
Her Data mining research incorporates themes from Evolving networks and Urban economics.
Her Machine learning research is multidisciplinary, relying on both Class, Semantics, Algorithm design and Image.
Her Anomaly detection research incorporates elements of Radial basis function kernel, Autoencoder, Outlier, Cluster analysis and Ensemble learning.
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