Carlotta Domeniconi mainly investigates Artificial intelligence, Pattern recognition, Data mining, Cluster analysis and Machine learning. Artificial intelligence is frequently linked to Set in her study. The concepts of her Pattern recognition study are interwoven with issues in Clustering high-dimensional data and Curse of dimensionality.
Her Data mining study combines topics from a wide range of disciplines, such as Estimator and Kernel density estimation. Her Cluster analysis research integrates issues from Intrusion detection system, Transduction and Outlier. Her study in Machine learning is interdisciplinary in nature, drawing from both Topic model, Latent Dirichlet allocation, Protein function prediction and Benchmark.
Carlotta Domeniconi spends much of her time researching Artificial intelligence, Cluster analysis, Data mining, Machine learning and Pattern recognition. Her Artificial intelligence research incorporates themes from Protein function prediction, Set and Natural language processing. Her work deals with themes such as Ranking, Supervised learning and Outlier, which intersect with Data mining.
The study incorporates disciplines such as Crowdsourcing, Latent Dirichlet allocation, Topic model and Robustness in addition to Machine learning. Her k-nearest neighbors algorithm and Dimensionality reduction study in the realm of Pattern recognition connects with subjects such as Linear subspace. Her studies deal with areas such as Nearest neighbor search and Nearest-neighbor chain algorithm as well as k-nearest neighbors algorithm.
Carlotta Domeniconi mainly investigates Artificial intelligence, Data mining, Cluster analysis, Matrix decomposition and Machine learning. Her research combines Pattern recognition and Artificial intelligence. As a part of the same scientific study, Carlotta Domeniconi usually deals with the Pattern recognition, concentrating on Semantic similarity and frequently concerns with Multi-label classification, Zero shot learning and Word embedding.
Her research in the fields of Relation overlaps with other disciplines such as Redundancy. Her study in Cluster analysis is interdisciplinary in nature, drawing from both Data point, Rule of thumb, Reduction and Data science. Her Machine learning study integrates concerns from other disciplines, such as Crowdsourcing and Robustness.
Carlotta Domeniconi focuses on Matrix decomposition, Data mining, Heterogeneous network, Cluster analysis and Relational database. Her Data mining research is multidisciplinary, relying on both Ranking and Hash function. Her studies link Data science with Cluster analysis.
Her Relational database study incorporates themes from Sensor fusion and Disease Association. The Embedding study combines topics in areas such as Machine learning, Discriminative model and Convolutional neural network. Her Discriminative model study results in a more complete grasp of Artificial intelligence.
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On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking
L. AlSumait;D. Barbara;C. Domeniconi.
international conference on data mining (2008)
Locally adaptive metric nearest-neighbor classification
C. Domeniconi;Jing Peng;D. Gunopulos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Locally adaptive metrics for clustering high dimensional data
Carlotta Domeniconi;Dimitrios Gunopulos;Sheng Ma;Bojun Yan.
Data Mining and Knowledge Discovery (2007)
Building semantic kernels for text classification using wikipedia
Pu Wang;Carlotta Domeniconi.
knowledge discovery and data mining (2008)
Approximating multi-dimensional aggregate range queries over real attributes
Dimitrios Gunopulos;George Kollios;Vassilis J. Tsotras;Carlotta Domeniconi.
international conference on management of data (2000)
Incremental support vector machine construction
C. Domeniconi;D. Gunopulos.
international conference on data mining (2001)
Non-linear dimensionality reduction techniques for classification and visualization
Michail Vlachos;Carlotta Domeniconi;Dimitrios Gunopulos;George Kollios.
knowledge discovery and data mining (2002)
Weighted cluster ensembles: Methods and analysis
Carlotta Domeniconi;Muna Al-Razgan.
ACM Transactions on Knowledge Discovery From Data (2009)
Subspace Clustering of High Dimensional Data.
Carlotta Domeniconi;Dimitris Papadopoulos;Dimitrios Gunopulos;Sheng Ma.
siam international conference on data mining (2004)
Topic Significance Ranking of LDA Generative Models
Loulwah Alsumait;Daniel Barbará;James Gentle;Carlotta Domeniconi.
european conference on machine learning (2009)
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