SIGKDD(KDD) 2003 论文列表
Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003.
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Applying data mining in investigating money laundering crimes.
Experimental study of discovering essential information from customer inquiry.
Visualizing concept drift.
Data-driven validation, completion and construction of event relationship networks.
Towards NIC-based intrusion detection.
Experimental design for solicitation campaigns.
Similarity analysis on government regulations.
Data quality through knowledge engineering.
Architecting a knowledge discovery engine for military commanders utilizing massive runs of simulations.
An adaptive nearest neighbor search for a parts acquisition ePortal.
Efficiently handling feature redundancy in high-dimensional data.
Distributed multivariate regression based on influential observations.
Time and sample efficient discovery of Markov blankets and direct causal relations.
PaintingClass: interactive construction, visualization and exploration of decision trees.
Weighted Association Rule Mining using weighted support and significance framework.
Mining phenotypes and informative genes from gene expression data.
Improving spatial locality of programs via data mining.
New unsupervised clustering algorithm for large datasets.
Carpenter: finding closed patterns in long biological datasets.
Graph-based anomaly detection.
Learning relational probability trees.
Distributed cooperative mining for information consortia.
Online novelty detection on temporal sequences.
On computing, storing and querying frequent patterns.
Mining data records in Web pages.
Empirical comparisons of various voting methods in bagging.
A two-way visualization method for clustered data.
Nantonac collaborative filtering: recommendation based on order responses.
A bag of paths model for measuring structural similarity in Web documents.
Efficient decision tree construction on streaming data.
Interactive exploration of coherent patterns in time-series gene expression data.
Playing hide-and-seek with correlations.
Mining viewpoint patterns in image databases.
Navigating massive data sets via local clustering.
Natural communities in large linked networks.
A Web page prediction model based on click-stream tree representation of user behavior.
Correlating synchronous and asynchronous data streams.
Accurate decision trees for mining high-speed data streams.
Experiments with random projections for machine learning.
Applications of sampling and fractional factorial designs to model-free data squashing.
Using randomized response techniques for privacy-preserving data mining.
Understanding captions in biomedical publications.
Probabilistic discovery of time series motifs.
Finding recent frequent itemsets adaptively over online data streams.
Mining high dimensional data for classifier knowledge.
Style mining of electronic messages for multiple authorship discrimination: first results.
The anatomy of a multimodal information filter.
Knowledge-based data mining.
Discovery of climate indices using clustering.
Frequent-subsequence-based prediction of outer membrane proteins.
Critical event prediction for proactive management in large-scale computer clusters.
Clinical and financial outcomes analysis with existing hospital patient records.
Capturing best practice for microarray gene expression data analysis.
Passenger-based predictive modeling of airline no-show rates.
The data mining approach to automated software testing.
Information awareness: a prospective technical assessment.
Mining hepatitis data with temporal abstraction.
Empirical Bayesian data mining for discovering patterns in post-marketing drug safety.
Golden Path Analyzer: using divide-and-conquer to cluster Web clickstreams.
Efficient elastic burst detection in data streams.
Fast vertical mining using diffsets.
XRules: an effective structural classifier for XML data.
Classifying large data sets using SVMs with hierarchical clusters.
Eliminating noisy information in Web pages for data mining.
CloseGraph: mining closed frequent graph patterns.
Screening and interpreting multi-item associations based on log-linear modeling.
Algorithms for estimating relative importance in networks.
On detecting differences between groups.
Mining unexpected rules by pushing user dynamics.
CLOSET+: searching for the best strategies for mining frequent closed itemsets.
Mining concept-drifting data streams using ensemble classifiers.
Indexing multi-dimensional time-series with support for multiple distance measures.
Privacy-preserving k-means clustering over vertically partitioned data.
Assessment and pruning of hierarchical model based clustering.
Generating English summaries of time series data using the Gricean maxims.
Cross-training: learning probabilistic mappings between topics.
Aggregation-based feature invention and relational concept classes.
Visualizing changes in the structure of data for exploratory feature selection.
PROXIMUS: a framework for analyzing very high dimensional discrete-attributed datasets.
Maximizing the spread of influence through a social network.
Fragments of order.
To buy or not to buy: mining airfare data to minimize ticket purchase price.
Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining.
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process.
Information-theoretic co-clustering.
Translation-invariant mixture models for curve clustering.
Extracting semantics from data cubes using cube transversals and closures.
Efficient data reduction with EASE.
An iterative hypothesis-testing strategy for pattern discovery.
Adaptive duplicate detection using learnable string similarity measures.
Mining distance-based outliers in near linear time with randomization and a simple pruning rule.
Generative model-based clustering of directional data.
Towards systematic design of distance functions for data mining applications.
Analyzing customer behavior at Amazon.com.
Statistical learning from relational data.
On-line science: the world-wide telescope as a prototype for the new computational science.
