Philip S. Yu

Publications1,748
h-index143
Citations97,456
Highly Influential Citations7,372
Author pages are created from data sourced from our academic publisher partnerships and public sources.

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A Framework for Clustering Evolving Data Streams
Transfer Feature Learning with Joint Distribution Adaptation
TLDR
JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference.
Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN,…
A Comprehensive Survey on Graph Neural Networks
TLDR
This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields and proposes a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNS, convolutional GNN’s, graph autoencoders, and spatial–temporal Gnns.
PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
TLDR
Under the meta path framework, a novel similarity measure called PathSim is defined that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures.
Mining concept-drifting data streams using ensemble classifiers
TLDR
This paper proposes a general framework for mining concept-drifting data streams using weighted ensemble classifiers, and shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
Fast algorithms for projected clustering
TLDR
An algorithmic framework for solving the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves, is developed and tested.
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
TLDR
A deep model to learn item properties and user behaviors jointly from review text, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers.
A holistic lexicon-based approach to opinion mining
TLDR
This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
BLINKS: ranked keyword searches on graphs
TLDR
BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search, and offers orders-of-magnitude performance improvement over existing approaches.
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