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A Framework for Clustering Evolving Data Streams
Transfer Feature Learning with Joint Distribution Adaptation
- Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
- Computer ScienceIEEE International Conference on Computer Vision
- 1 December 2013
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
- Xindong Wu, Vipin Kumar, D. Steinberg
- Computer ScienceKnowledge and Information Systems
- 19 December 2007
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
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
- Computer ScienceIEEE Transactions on Neural Networks and Learning…
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
- Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, Tianyi Wu
- Computer ScienceProc. VLDB Endow.
- 1 August 2011
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
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
- C. Aggarwal, C. Procopiuc, J. Wolf, Philip S. Yu, Jong Soo Park
- Computer ScienceSIGMOD '99
- 1 June 1999
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
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
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
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.