Philip S. Yu

Publications
Influence

A Framework for Clustering Evolving Data Streams

C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu
Computer Science
VLDB
9 September 2003

This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. Expand

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Transfer Feature Learning with Joint Distribution Adaptation

Mingsheng Long, J. Wang, Guiguang Ding, J. Sun, Philip S. Yu
Mathematics, Computer Science
IEEE International Conference on Computer Vision
1 December 2013

We put forward a novel transfer learning solution, referred to as Joint Distribution Adaptation (JDA), to jointly adapt both the marginal and conditional distributions in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Expand

Top 10 algorithms in data mining

X. Wu, V. Kumar, +11 authors D. Steinberg
Computer Science
Knowledge and Information Systems
19 December 2007

This paper presents the top 10 data mining algorithms identified by the International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Expand

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PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks

Y. Sun, Jiawei Han, X. Yan, Philip S. Yu, Tianyi Wu
Computer Science
Proc. VLDB Endow.
1 August 2011

We introduce a meta path-based similarity framework for objects that are defined among the same type of objects in heterogeneous networks. Expand

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Mining concept-drifting data streams using ensemble classifiers

H. Wang, W. Fan, Philip S. Yu, Jiawei Han
Computer Science
KDD '03
24 August 2003

We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream using weighted ensemble classifiers. Expand

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Fast algorithms for projected clustering

C. Aggarwal, C. Procopiuc, J. Wolf, Philip S. Yu, J. S. Park
Computer Science
SIGMOD '99
1 June 1999

The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Expand

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A holistic lexicon-based approach to opinion mining

X. Ding, B. Liu, Philip S. Yu
Computer Science
WSDM '08
11 February 2008

We propose 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. Expand

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A Comprehensive Survey on Graph Neural Networks

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, C. Zhang, Philip S. Yu
Computer Science, Mathematics
IEEE Transactions on Neural Networks and Learning…
3 January 2019

We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent, convolutional, graph autoencoders, and spatial–temporalGNNs. Expand

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Building text classifiers using positive and unlabeled examples

B. Liu, Y. Dai, X. Li, Wee Sun Lee, Philip S. Yu
Computer Science
Third IEEE International Conference on Data…
19 November 2003

This paper studies the problem of building two-class classifiers with only positive and unlabeled examples, but no negative examples. Expand

BLINKS: ranked keyword searches on graphs

Hao He, H. Wang, Jun Yang, Philip S. Yu
Computer Science
SIGMOD '07
11 June 2007

We propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs, which provides orders-of-magnitude performance improvement over existing approaches. Expand

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