Proceedings of the Sixth SIAM International Conference on Data MiningJoydeep Ghosh SIAM, 2006 M04 1 - 658 pages The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them. |
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Contents
On the Necessary and Sufficient Conditions of a Meaningful Distance Function for High Dimensional | 12 |
Transform Regression and the Kolmogorov Superposition Theorem | 35 |
Deriving Private Information from Randomly Perturbed Ratings | 59 |
Automated Knowledge Discovery from Simulators | 82 |
Mining Control Flow Abnormality for Logic Error Isolation | 106 |
An Efficient Method for Generating | 130 |
KMeans Clustering over a Large Dynamic Network | 153 |
Contents | 154 |
Collaborative Information Extraction and Mining from Multiple Web Documents | 442 |
Cluster Description Formats Problems and Algorithms | 464 |
Bayesian KMeans as a MaximizationExpectation Algorithm | 474 |
Cone Cluster Labeling for Support Vector Clustering | 484 |
A New PrivacyPreserving Distributed kClustering Algorithm | 494 |
Detecting the Change of Clustering Structure in Categorical Data Streams | 504 |
Transductive Denoising and Dimensionality Reduction Using Total Bregman Regression | 514 |
Fast Optimal Bandwidth Selection for Kernel Density Estimation | 524 |
Exploring Prototypes for Classification | 176 |
A Semantic Approach for Mining Hidden Links from Complementary and Noninteractive | 200 |
Mining Frequent Agreement Subtrees in Phylogenetic Databases | 222 |
Trend Relational Analysis and GreyFuzzy Clustering Method | 234 |
Weighted Clustering Ensembles | 258 |
A TopDown Row Enumeration | 282 |
Discovery of Coevolving Spatial Event Sets | 306 |
DensityBased Clustering over an Evolving Data Stream with Noise | 328 |
Efficient Mining of Temporally Annotated Sequences | 348 |
Segmentation and Dimensionality Reduction | 372 |
Item Sets That Compress | 395 |
Mining Frequent Closed Itemsets Out of Core | 419 |
On Approximate Solutions to Support Vector Machines | 534 |
Inference of Node Replacement Recursive Graph Grammars | 544 |
Health Monitoring of a Shaft Transmission System via Hybrid Models of PCR and | 554 |
A Systematic CrossComparison of Sequence Classifiers | 564 |
GraphBased Methods for Orbit Classification | 574 |
Profiling Protein Families from Partially Aligned Sequences | 584 |
Mining Novel Association Rules from Text | 589 |
Using Compression to Identify Classes of Inauthentic Texts | 604 |
Robust Clustering for Tracking Noisy Evolving Data Streams | 619 |
Finding Sequential Patterns from Massive Number of Spatiotemporal Events | 634 |
Common terms and phrases
accuracy analysis applications approach approximation assigned average bitmap candidate centroids classification closed itemsets cluster centers collaborative filtering computed concepts corresponding data mining data points data set data streams database DBLP defined denoted density dimensional dimensionality reduction distance function distribution documents efficient error estimate evaluation example extraction feature Figure frequent itemsets given global graph hyperplane initial input itemsets iteration K-means K-means algorithm K-medians label Lemma linear Machine Learning Markov network matrix measure method minimal minsup mixture model MovieLens neighbors node noise number of clusters objects optimal outliers output paper parameters partition patterns performance problem Proc proposed prototype models pruning random Raynaud Disease references represent sample Section segmentation semantic types sequence shows similarity space statistics step string structure subtree support vector machines synsets Table technique Theorem threshold tion transactions tree tuple tuples values variables vector weight