ICDM 2007 - PDF Preprint Papers

ICDM 2007 IEEE International Conference on Data Mining

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ICDM
2007
IEEE

476views Data Mining» more ICDM 2007»

FiVaTech: Page-Level Web Data Extraction from Template Pages

14 years 6 months ago

Download www.csie.ncu.edu.tw

In this paper, we proposed a new approach, called FiVaTech for the problem of Web data extraction. FiVaTech is a page-level data extraction system which deduces the data schema an...

Mohammed Kayed, Chia-Hui Chang, Khaled F. Shaalan,...

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Id	Views	Title	Status

1	476	FiVaTech: Page-Level Web Data Extraction from Template Pages	claim
2	301	Stream Event Detection: A Unified Framework for Mining Outlier, Change and Burst Simultaneously over Data Stream	claim
3	289	Latent Dirichlet Conditional Naive-Bayes Models	claim
4	276	SOPS: Stock Prediction Using Web Sentiment	claim
5	254	Sampling for Sequential Pattern Mining: From Static Databases to Data Streams	claim
6	248	Adapting SVM Classifiers to Data with Shifted Distributions	claim
7	227	Optimal Subsequence Bijection	claim
8	206	A Novel Rule Weighting Approach in Classification Association Rule Mining	claim
9	199	Discovering Structural Anomalies in Graph-Based Data	claim
10	198	Social Network Extraction of Academic Researchers	claim
11	197	Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks	claim
12	196	Diagnosing Similarity of Oscillation Trends in Time Series	claim
13	192	Discovering Temporal Communities from Social Network Documents	claim
14	187	Statistical Learning Algorithm for Tree Similarity	claim
15	187	A Comparative Study of Methods for Transductive Transfer Learning	claim
16	186	Mining Interpretable Human Strategies: A Case Study	claim
17	184	Bayesian Folding-In with Dirichlet Kernels for PLSI	claim
18	183	How Much Noise Is Too Much: A Study in Automatic Text Classification	claim
19	183	Depth-Based Novelty Detection and Its Application to Taxonomic Research	claim
20	182	Co-ranking Authors and Documents in a Heterogeneous Network	claim
21	180	Mining Frequent Itemsets in a Stream	claim
22	179	GDClust: A Graph-Based Document Clustering Technique	claim
23	176	A Compact Representation of Spatio-Temporal Data	claim
24	175	gApprox: Mining Frequent Approximate Patterns from a Massive Network	claim
25	174	Targeting Input Data for Acoustic Bird Species Recognition Using Data Mining and HMMs	claim
26	173	Sparse Word Graphs: A Scalable Algorithm for Capturing Word Correlations in Topic Models	claim
27	170	Consensus Clusterings	claim
28	169	Efficient Discovery of Frequent Approximate Sequential Patterns	claim
29	166	Mining Statistical Information of Frequent Fault-Tolerant Patterns in Transactional Databases	claim
30	166	A Divisive Hierarchical Structural Clustering Algorithm for Networks	claim
31	162	Exploiting Network Structure for Active Inference in Collective Classification	claim
32	161	Experimental Comparison of Feature Subset Selection Methods	claim
33	161	Segmenting Multi-attribute Sequences Using Dynamic Bayesian Networks	claim
34	159	Incremental Subspace Clustering over Multiple Data Streams	claim
35	159	Spectral Regression: A Unified Approach for Sparse Subspace Learning	claim
36	158	On Appropriate Assumptions to Mine Data Streams: Analysis and Practice	claim
37	157	Failure Prediction in IBM BlueGene/L Event Logs	claim
38	157	Training Conditional Random Fields by Periodic Step Size Adaptation for Large-Scale Text Mining	claim
39	156	Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors	claim
40	155	Aspect Summarization from Blogsphere for Social Study	claim
41	155	Transitional Patterns and Their Significant Milestones	claim
42	154	Cocktail Ensemble for Regression	claim
43	153	HSN-PAM: Finding Hierarchical Probabilistic Groups from Large-Scale Networks	claim
44	151	Combining Collective Classification and Link Prediction	claim
45	150	Connections between Mining Frequent Itemsets and Learning Generative Models	claim
46	149	Temporal Analysis of Semantic Graphs Using ASALSAN	claim
47	149	Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization	claim
48	149	Non-redundant Multi-view Clustering via Orthogonalization	claim
49	149	Extracting Author Meta-Data from Web Using Visual Features	claim
50	148	Binary Matrix Factorization with Applications	claim
51	148	Sample Selection for Maximal Diversity	claim
52	147	Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights	claim
53	147	Improving Knowledge Discovery in Document Collections through Combining Text Retrieval and Link Analysis Techniques	claim
54	145	Using Data Mining to Estimate Missing Sensor Data	claim
55	143	Bit Sequences and Biclustering of Text Documents	claim
56	140	Finding Cohesive Clusters for Analyzing Knowledge Communities	claim
57	140	Sequential Change Detection on Data Streams	claim
58	139	Data Clustering with a Relational Push-Pull Model	claim
59	138	Bandit-Based Algorithms for Budgeted Learning	claim
60	138	Modeling Fundamental Geo-Raster Operations with Array Algebra	claim
61	138	Preserving Privacy through Data Generation	claim
62	137	Locally Constrained Support Vector Clustering	claim
63	136	Dynamic Micro Targeting: Fitness-Based Approach to Predicting Individual Preferences	claim
64	136	Recommendation via Query Centered Random Walk on K-Partite Graph	claim
65	136	Data Discretization Unification	claim
66	135	Private Inference Control for Aggregate Database Queries	claim
67	134	On Regional Association Rule Scoping	claim
68	133	Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?	claim
69	133	Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval	claim
70	132	Granularity Conscious Modeling for Probabilistic Databases	claim
71	132	Error-Aware Density-Based Clustering of Imprecise Measurement Values	claim
72	132	Learning What Makes a Society Tick	claim
73	131	Parallel Mining of Frequent Closed Patterns: Harnessing Modern Computer Architectures	claim
74	131	Characterizing RNA Secondary-Structure Features and Their Effects on Splice-Site Prediction	claim
75	131	Predicting and Optimizing Classifier Utility with the Power Law	claim
76	129	Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets	claim
77	129	A Generalization of Proximity Functions for K-Means	claim
78	129	Semi-supervised Clustering Using Bayesian Regularization	claim
79	129	Feature Selection for Nonlinear Kernel Support Vector Machines	claim
80	127	Efficient Algorithms for Mining Significant Substructures in Graphs with Quality Guarantees	claim
81	127	Automatically Finding Images for Clinical Decision Support	claim
82	126	Exploration of Link Structure and Community-Based Node Roles in Network Analysis	claim
83	126	Ask the Crowd to Find out What's Important	claim
84	124	Local Probabilistic Models for Link Prediction	claim
85	124	Community Learning by Graph Approximation	claim
86	122	Zonal Co-location Pattern Discovery with Dynamic Parameters	claim
87	122	Noise Modeling with Associative Corruption Rules	claim
88	122	Representing Tuple and Attribute Uncertainty in Probabilistic Databases	claim
89	121	Finding Predictive Runs with LAPS	claim
90	121	ORIGAMI: Mining Representative Orthogonal Graph Patterns	claim
91	119	Detecting Fractures in Classifier Performance	claim
92	119	Reducing UK-Means to K-Means	claim
93	118	Subgraph Support in a Single Large Graph	claim
94	118	Predictive Data Mining for Lung Nodule Interpretation	claim
95	118	Web Site Recommendation Using HTTP Traffic	claim
96	116	Cross-Mining Binary and Numerical Attributes	claim
97	116	A Computational Approach to Style in American Poetry	claim
98	116	Privacy-Preserving k-NN for Small and Large Data Sets	claim
99	114	Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping Files	claim
100	111	Change-Point Detection in Time-Series Data Based on Subspace Identification	claim
101	109	A Support Vector Approach to Censored Targets	claim
102	109	Language-Independent Set Expansion of Named Entities Using the Web	claim
103	106	High-Speed Function Approximation	claim
104	105	Fast Mining of Complex Spatial Co-location Patterns Using GLIMIT	claim
105	104	Secure Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases	claim
106	103	Mechanism Design for Clustering Aggregation by Selfish Systems	claim
107	103	An Examination of Experimental Methodology for Classifiers of Relational Data	claim
108	101	Lightweight Distributed Trust Propagation	claim
109	99	Optimizing Frequency Queries for Data Mining Applications	claim
110	98	General Averaged Divergence Analysis	claim
111	97	Supervised Learning by Training on Aggregate Outputs	claim
112	96	The Chosen Few: On Identifying Valuable Patterns	claim
113	95	Incremental Quantization for Aging Data Streams	claim

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