NDLI: Information extraction, data mining and joint inference

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Self-Organizing wireless sensor networks in action

Deriving quantitative models for correlation clusters

Outlier detection by active learning

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Computer aided detection via asymmetric cascade of sparse hyperplane classifiers

Identifying "best bet" web search results by mining past user behavior

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New cached-sufficient statistics algorithms for quickly answering statistical questions

Learning to rank networked entities

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Onboard classifiers for science event detection on a remote sensing spacecraft

Mining citizen science data to predict orevalence of wild bird species

Spatial scan statistics: approximations and performance study

CCCS: a top-down associative classifier for imbalanced class distribution

Information extraction, data mining and joint inference

Pragmatic text mining: minimizing human effort to quantify many issues in call logs

A component-based framework for knowledge discovery in bioinformatics

Global distance-based segmentation of trajectories

A framework for analysis of dynamic social networks

Data mining challenges in the automotive domain

Mining for proposal reviewers: lessons learned at the national science foundation

Discovering significant OPSM subspace clusters in massive gene expression data

Group formation in large social networks: membership, growth, and evolution

Query-time entity resolution

GPLAG: detection of software plagiarism by program dependence graph analysis

Maximum profit mining and its application in software development

Detecting outliers using transduction and statistical testing

Model compression

Understandable models Of music collections based on exhaustive feature generation with temporal statistics

YALE: rapid prototyping for complex data mining tasks

Robust information-theoretic clustering

Classification features for attack detection in collaborative recommender systems

Opportunity map: identifying causes of failure - a deployed data mining system

Camouflaged fraud detection in domains with complex relationships

Efficient anonymity-preserving data collection

Single-pass online learning: performance, voting schemes and online feature selection

Beyond classification and ranking: constrained optimization of the ROI

Out-of-core frequent pattern mining on a commodity PC

Evolutionary clustering

Mining rank-correlated sets of numerical attributes

Algorithms for discovering bucket orders from data

NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs

Mining relational data through correlation-based multiple view validation

Estimating the global pagerank of web communities

Recommendation method for extending subscription periods

Orthogonal nonnegative matrix t-factorizations for clustering

Dynamic, real-time forecasting of online auctions via functional models

A general framework for accurate and fast regression by data summarization in random decision trees

Polynomial association rules with applications to logistic regression

Reverse testing: an efficient framework to select amongst classifiers under sample selection bias

CFI-Stream: mining closed frequent itemsets in data streams

Quantifying trends accurately despite classifier error and class imbalance

Reducing the human overhead in text categorization

Assessing data mining results via swap randomization

Algorithms for storytelling

A new efficient probabilistic model for mining labeled ordered trees

Structure and evolution of online social networks

Learning the unified kernel machines for classification

Cryptographically private support vector machines

Frequent subgraph mining in outerplanar graphs

Bias and controversy: beyond the statistical deviation

Adaptive event detection with time-varying poisson processes

Sampling from large graphs

Training linear SVMs in linear time

Clustering pair-wise dissimilarity data into partially ordered sets

Mining quantitative correlated patterns using an information-theoretic approach

Visual data mining using principled projection algorithms and information visualization techniques

Maximally informative k-itemsets and their efficient discovery

A mixture model for contextual text mining

Measuring and extracting proximity in networks

A new multi-view regression approach with an application to customer wallet estimation

Hierarchical topic segmentation of websites

Efficient multidimensional data representations based on multiple correspondence analysis

New EM derived from Kullback-Leibler divergence

Algorithms for time series knowledge mining

Workload-aware anonymization

Clustering based large margin classification: a scalable approach using SOCP formulation

Very sparse random projections

Statistical entity-topic models

Rule interestingness analysis using OLAP operations

Mining for misconfigured machines in grid systems

Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

Automatic mining of fruit fly embryo images

Unsupervised learning on k-partite graphs

Naïve filterbots for robust cold-start recommendations

Tensor-CUR decompositions for tensor-based data

MONIC: modeling and monitoring cluster transitions

Generating semantic annotations for frequent patterns with context analysis

Combining linguistic and statistical analysis to extract relations from web documents

Aggregating time partitions

Mining long-term search history to improve search accuracy

Using structure indices for efficient approximation of network properties

Efficient kernel feature extraction for massive data sets

Learning sparse metrics via linear programming

Summarizing itemset patterns using probabilistic models

Beyond streams and graphs: dynamic tensor analysis

Suppressing model overfitting in mining concept-drifting data streams

Acclimatizing Taxonomic Semantics for Hierarchical Content Classification

A large-scale analysis of query logs for assessing personalization opportunities

Mining distance-based outliers from large databases in any metric space

Semi-supervised time series classification

Center-piece subgraphs: problem definition and fast solutions

(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

Anonymizing sequential releases

Incremental approximate matrix factorization for speeding up support vector machines

Topics over time: a non-Markov continuous-time model of topical trends

Outlier detection by sampling with accuracy guarantees

Discovering significant rules

Discovering interesting patterns through user's interactive feedback

Extracting redundancy-aware top-k patterns

K-means clustering versus validation measures: a data distribution perspective

Regularized discriminant analysis for high dimensional, low sample size data

Utility-based anonymization using local recoding

Supervised probabilistic principal component analysis

Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering

Extracting key-substring-group features for text classification

Coherent closed quasi-clique discovery from large dense graph databases

Event detection from evolution of click-through data

Mining progressive confident rules

Simultaneous record detection and attribute labeling in web data extraction

Attack detection in time series for recommender systems

Identifying bridging rules between conceptual clusters

Linear prediction models with graph regularization for web-page categorization

BLOSOM: a framework for mining arbitrary boolean expressions

Information extraction, data mining and joint inference

Content Provider	ACM Digital Library
Author	McCallum, Andrew
Abstract	Although information extraction and data mining appear together in many applications, their interface in most current systems would better be described as serial juxtaposition than as tight integration. Information extraction populates slots in a database by identifying relevant subsequences of text, but is usually not aware of the emerging patterns and regularities in the database. Data mining methods begin from a populated database, and are often unaware of where the data came from, or its inherent uncertainties. The result is that the accuracy of both suffers, and accurate mining of complex text sources has been beyond reach.In this talk I will describe work in probabilistic models that perform joint inference across multiple components of an information processing pipeline in order to avoid the brittle accumulation of errors. After briefly introducing conditional random fields, I will describe recent work in information extraction leveraging factorial state representations, entity resolution, and transfer learning, as well as scalable methods of inference and learning. I'll close with some recent work on probabilistic models for social network analysis, and a demonstration of Rexa.info, a new research paper search engine.
Starting Page	835
Ending Page	835
Page Count	1
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150515
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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