NDLI: Learning functional dependency networks based on genetic programming

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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

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Learning instance greedily cloning naive Bayes for ranking

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

Stability of feature selection algorithms

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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

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

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Alternate representation of distance matrices for characterization of protein structure

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

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Parameter-free spatial data mining using MDL

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Mining patterns of change in remote sensing image databases

Ranking-based evaluation of regression models

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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

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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

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Mining chains of relations

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Blocking anonymity threats raised by frequent itemset mining

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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

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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

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Triple jump acceleration for the EM algorithm

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Pairwise symmetry decomposition method for generalized covariance analysis

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Supervised ordering - an empirical survey

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Partial elastic matching of time series

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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

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Text representation: from vector to tensor

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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)

Third IEEE International Conference on Data Mining

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

Proceedings 2001 IEEE International Conference on Data Mining

Learning functional dependency networks based on genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Wing-Ho Shum Kwong-Sak Leung Man-Leung Wong
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., The Chinese Univ. of Hong Kong, China (Wing-Ho Shum; Kwong-Sak Leung)
Abstract	Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.
File Size	544758
File Format	PDF
ISBN	0769522785
ISSN	15504786
DOI	10.1109/ICDM.2005.86
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	Genetic programming Bayesian methods History Computer science Power engineering and energy Computer networks Power engineering computing Robustness Educational institutions Biology
Content Type	Text
Resource Type	Article

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