NDLI: Text classification with evolving label-sets

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Top 10 data mining mistakes

Handling generalized cost functions in the partitioning optimization problem through sequential binary programming

Online hierarchical clustering in a data warehouse environment

eMailSift: eMail classification based on structure and content

An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays

Classifier fusion using shared sampling distribution for boosting

Improving automatic query classification via semi-supervised learning

ViVo: visual vocabulary construction for mining biomedical images

Adaptive product normalization: using online learning for record linkage in comparison shopping

Using information-theoretic measures to assess association rule interestingness

Shortest-path kernels on graphs

Mining frequent spatio-temporal sequential patterns

Modeling multiple time series for anomaly detection

Summarization - compressing data into an informative representation

Labeling unclustered categorical data into clusters based on the important attribute values

Making subsequence time series clustering meaningful

Kernel-density-based clustering of time series subsequences using a continuous random-walk noise model

Usage-based PageRank for Web personalization

WARP: time warping for periodicity detection

Bifold constraint-based mining by simultaneous monotone and anti-monotone checking

Effective estimation of posterior probabilities: explaining the accuracy of randomized decision tree approaches

A thorough experimental study of datasets for frequent itemsets

AMIOT: induced ordered tree mining in tree-structured databases

Hierarchy-regularized latent semantic indexing

Extracting frequent subsequences from a single long data sequence a novel anti-monotonic measure and a simple on-line algorithm

Mining minimal distinguishing subsequence patterns with gap constraints

Learning instance greedily cloning naive Bayes for ranking

An algorithm for in-core frequent itemset mining on streaming data

Stability of feature selection algorithms

HOT SAX: efficiently finding the most unusual time series subsequence

Orthogonal neighborhood preserving projections

Higher-order Web link analysis using multilinear algebra

A generic framework for efficient subspace clustering of high-dimensional data

Effective and efficient distributed model-based clustering

Finding maximal frequent itemsets over online data streams adaptively

CanTree: a tree structure for efficient incremental mining of frequent patterns

Combining multiple clusterings by soft correspondence

A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment

Alternate representation of distance matrices for characterization of protein structure

Training support vector machines using Gilbert's algorithm

A heterogeneous field matching method for record linkage

Leveraging relational autocorrelation with latent group models

Balancing exploration and exploitation: a new algorithm for active machine learning

Finding representative set from massive data

Parameter-free spatial data mining using MDL

Discovering frequent arrangements of temporal intervals

Mining patterns of change in remote sensing image databases

Ranking-based evaluation of regression models

Compound classification models for recommender systems

Multi-stage classification

Learning functional dependency networks based on genetic programming

Generalizing the notion of confidence [Mining association rules]

SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

Neighborhood formation and anomaly detection in bipartite graphs

A border-based approach for hiding sensitive frequent itemsets

X-mHMM: an efficient algorithm for training mixtures of HMMs when the number of mixtures is unknown

A random walk through human associations

Supervised tensor learning

A Bernoulli relational model for nonlinear embedding

Template-based privacy preservation in classification problems

On reducing classifier granularity in mining concept-drifting data streams

Approximate inverse frequent itemset mining: privacy, complexity, and approximation

Atomic wedgie: efficient query filtering for streaming time series

Discriminatively trained Markov model for sequence classification

Integrating hidden Markov models and spectral analysis for sensory time series clustering

Discriminant analysis: a unified approach

Sharing classifiers among ensembles from related problem domains

A visual data mining framework for convenient identification of useful knowledge

Efficient text classification by weighted proximal SVM

A rule evaluation support method with learning models based on objective rule evaluation indexes

Mining chains of relations

A preference model for structured supervised learning tasks

Blocking anonymity threats raised by frequent itemset mining

Adaptive clustering: obtaining better clusters using feedback and past experience

Semi-supervised mixture of kernels via LPBoost methods

A levelwise search algorithm for interesting subspace clusters

Segment-based injection attacks against collaborative filtering recommender systems

On feature selection through clustering

Sequential pattern mining in multiple streams

Privacy preserving data classification with rotation perturbation

A computational framework for taxonomic research: diagnosing body shape within fish species complexes

Obtaining best parameter values for accurate classification

Process diagnosis via electrical-wafer-sorting maps classification

An improved categorization of classifier's sensitivity on sample selection bias

Fast frequent string mining using suffix arrays

Privacy-preserving frequent pattern mining across private databases

CoLe: a cooperative data mining approach and its application to early diabetes detection

Feature selection for building cost-effective data stream classifiers

A scalable collaborative filtering framework based on co-clustering

Text classification with evolving label-sets

CloseMiner: discovering frequent closed itemsets using frequent closed tidsets

A framework for semi-supervised learning based on subjective and objective clustering criteria

Focused community discovery

Suppressing data sets to prevent discovery of association rules

Triple jump acceleration for the EM algorithm

Partial ensemble classifiers selection for better ranking

Pairwise symmetry decomposition method for generalized covariance analysis

FS/sup 3/: a random walk based free-form spatial scan statistic for anomalous window detection

Mining ontological knowledge from domain-specific text documents

Mining patterns that respond to actions

Supervised ordering - an empirical survey

Categorization and keyword identification of unlabeled documents

Gradual model generator for single-pass clustering

Making logistic regression a core data mining tool with TR-IRLS

Hierarchical density-based clustering of uncertain data

Semi-supervised clustering with metric learning using relative comparisons

On learning asymmetric dissimilarity measures

Partial elastic matching of time series

CLUGO: a clustering algorithm for automated functional annotations based on gene ontology

An optimal linear time algorithm for quasi-monotonic segmentation

Average number of frequent (closed) patterns in Bernoulli and Markovian databases

Predicting software escalations with maximum ROI

Mining approximate frequent itemsets from noisy data

Text representation: from vector to tensor

Parallel algorithms for distance-based and density-based outliers

Bit reduction support vector machine

Spatial clustering of chimpanzee locations for neighborhood identification

A graph-ranking algorithm for geo-referencing documents

An expected utility approach to active feature-value acquisition

Automatically mining result records from search engine response pages

Efficiently mining frequent closed partial orders

CLUMP: a scalable and robust framework for structure discovery

On the tractability of rule discovery from distributed data

Face recognition using landmark-based bidimensional regression

Instability of classifiers on categorical data

Pruning social networks using structural properties and descriptive attributes

Optimizing constraint-based mining by automatically relaxing constraints

Bias analysis in text classification for highly skewed data

Efficient mining of high branching factor attribute trees

Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework

Mining quantitative frequent itemsets using adaptive density-based subspace clustering

Hot item mining and summarization from multiple auction Web sites

Merging interface schemas on the deep Web via clustering aggregation

On the stationarity of multivariate time series for correlation-based data analysis

Speculative Markov blanket discovery for optimal feature selection

A join-less approach for co-location pattern mining: a summary of results

Learning through changes: an empirical study of dynamic behaviors of probability estimation trees

Visualizing global manifold based on distributed local data abstractions

Bagging with adaptive costs

Example-based robust outlier detection in high dimensional datasets

CTC - correlating tree patterns for classification

Fourth IEEE International Conference on Data Mining (ICDM'04)

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2002 IEEE International Conference on Data Mining, 2002. Proceedings.

Proceedings 2001 IEEE International Conference on Data Mining

Text classification with evolving label-sets

Content Provider	IEEE Xplore Digital Library
Author	Godbole, S. Ramakrishnan, G. Sarawagi, S.
Copyright Year	2005
Description	Author affiliation: IIT Bombay, India (Godbole, S.)
Abstract	We introduce the evolving label-set problem encountered in building real-world text classification systems. This problem arises when a text classification system trained on a label-set encounters documents of unseen classes at deployment time. We design a class-detector module that monitors unlabeled data, detects new classes, and suggests them to the administrator for inclusion in the label-set. We propose abstractions that group together tokens under human understandable concepts and provide a mechanism of assigning importance to unseen terms. We present generative algorithms leveraging the notion of support of documents in a model for (1) selecting documents of proposed new classes, and (2) automatically triggering detection of new classes. Experiments on three real world taxonomies show that our methods select new class documents with high precision, and trigger emergence of new classes with low false-positive and false-negative rates.
File Size	180985
File Format	PDF
ISBN	0769522785
ISSN	15504786
DOI	10.1109/ICDM.2005.143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text categorization Taxonomy Buildings Humans Constitution Australia Algorithm design and analysis Error analysis Robustness Data mining
Content Type	Text
Resource Type	Article

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