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SDM
2008
SIAM

256views Data Mining» more SDM 2008»

Graph Mining with Variational Dirichlet Process Mixture Models

13 years 5 months ago

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Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...

Koji Tsuda, Kenichi Kurihara

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Id	Views	Title	Status

1	256	Graph Mining with Variational Dirichlet Process Mixture Models	claim
2	206	Latent Variable Mining with Its Applications to Anomalous Behavior Detection	claim
3	197	A general framework for estimating similarity of datasets and decision trees: exploring semantic similarity of decision trees	claim
4	177	Robust Clustering in Arbitrarily Oriented Subspaces	claim
5	177	Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining	claim
6	177	Roughly Balanced Bagging for Imbalanced Data	claim
7	177	Cluster Ensemble Selection	claim
8	176	A General Model for Multiple View Unsupervised Learning	claim
9	168	Semi-Supervised Clustering via Matrix Factorization	claim
10	165	On the Dangers of Cross-Validation. An Experimental Evaluation	claim
11	164	Randomizing Social Networks: a Spectrum Preserving Approach	claim
12	161	Efficient Maximum Margin Clustering via Cutting Plane Algorithm	claim
13	158	Similarity Measures for Categorical Data: A Comparative Evaluation	claim
14	157	ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data	claim
15	150	A Stagewise Least Square Loss Function for Classification	claim
16	147	The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining	claim
17	144	Active Learning with Model Selection in Linear Regression	claim
18	144	Semi-supervised Multi-label Learning by Solving a Sylvester Equation	claim
19	140	Large-Scale Many-Class Learning	claim
20	140	Creating a Cluster Hierarchy under Constraints of a Partially Known Hierarchy	claim
21	139	Semi-Supervised Learning Based on Semiparametric Regularization	claim
22	139	Simultaneous Unsupervised Learning of Disparate Clusterings	claim
23	139	Maximal Quasi-Bicliques with Balanced Noise Tolerance: Concepts and Co-clustering Applications	claim
24	139	Proximity Tracking on Time-Evolving Bipartite Graphs	claim
25	138	Clustering from Constraint Graphs	claim
26	138	Learning Markov Network Structure using Few Independence Tests	claim
27	136	Exploration and Reduction of the Feature Space by Hierarchical Clustering	claim
28	135	A Spamicity Approach to Web Spam Detection	claim
29	135	Preemptive Measures against Malicious Party in Privacy-Preserving Data Mining	claim
30	134	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation	claim
31	133	A RELIEF Based Feature Extraction Algorithm	claim
32	133	Semantic Smoothing for Bayesian Text Classification with Small Training Data	claim
33	130	Mining Sequence Classifiers for Early Prediction	claim
34	129	Statistical Density Prediction in Traffic Networks	claim
35	125	Mining and Ranking Generators of Sequential Patterns	claim
36	124	Mining Association Rules of Simple Conjunctive Queries	claim
37	123	Constrained Co-clustering of Gene Expression Data	claim
38	122	Type-Independent Correction of Sample Selection Bias via Structural Discovery and Re-balancing	claim
39	121	Integration of Multiple Networks for Robust Label Propagation	claim
40	120	Spatial Scan Statistics for Graph Clustering	claim
41	119	An Efficient Local Algorithm for Distributed Multivariate Regression in Peer-to-Peer Networks	claim
42	118	Massive-Scale Kernel Discriminant Analysis: Mining for Quasars	claim
43	117	A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality	claim
44	114	Semi-Supervised Classification with Universum	claim
45	113	Randomization of real-valued matrices for assessing the significance of data mining results	claim
46	112	A pattern mining approach toward discovering generalized sequence signatures	claim
47	105	Gaussian Process Learning for Cyber-Attack Early Warning	claim
48	97	Efficient Distribution Mining and Classification	claim
49	95	Deterministic Latent Variable Models and Their Pitfalls	claim

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