On feature distributional clustering for text categorization

Bekkerman, Ron; El‐Yaniv, Ran; Tishby, Naftali; Winter, Yoad

doi:10.1145/383952.383976

Cited by 195 publications

(197 citation statements)

References 12 publications

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“…Corpus Summarization: Clustering techniques provide a coherent summary of the collection in the form of cluster-digests [83] or word-clusters [17,18], which can be used in order to provide summary insights into the overall content of the underlying corpus. Variants of such methods, especially sentence clustering, can also be used for document summarization, a topic, discussed in detail in Chapter 3.…”

Section: Document Organization and Browsingmentioning

confidence: 99%

“…In particular, word-clusters [17,18] and co-training methods [72] can be used in order to improve the classification accuracy of supervised applications with the use of clustering techniques.…”

Section: Document Classificationmentioning

confidence: 99%

“…A number of similar methods along this spirit are proposed in [4,14,35,47,89] with varying levels of supervision in the clustering process. Partially supervised clustering methods are also used feature transformation in classification using the methods as discussed in [17,18,88]. The idea is that the clustering structure provides a compressed feature space, which capture the relevant classification structure very well, and can therefore be helpful for classification.…”

Section: Semi-supervised Clusteringmentioning

confidence: 99%

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A Survey of Text Clustering Algorithms

2012

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Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

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Section: Document Organization and Browsingmentioning

confidence: 99%

Section: Document Classificationmentioning

confidence: 99%

Section: Semi-supervised Clusteringmentioning

confidence: 99%

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A Survey of Text Clustering Algorithms

2012

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“…Most usual cases where this technique is applied are gene selection from microarray data [13] [14] and text categorization [15] [16]. Confront the curse of dimensionality carries some recognized advantages like: reducing the measurement and storage requirements, facilitating visualization and understanding of data, diminishing training and predicting times and also improving prediction performance.…”

Section: Feature Selectionmentioning

confidence: 99%

“…these methods rank variables according to their individual predictive power. Pearson correlation [20], the coefficient of determination [13], information theoretic criteria [15] and partial least squares (PLS) [21] are used and correspond to this class.…”

Section: Feature Selectionmentioning

confidence: 99%

An Evolutionary Approach for Feature Selection applied to ADMET Prediction

Soto¹,

Cecchini²,

Vazquez³

et al. 2008

Int. Artif.

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Feature selection methods look for the selection of a subset of features or variables in a data set, such that these features are the most relevant for predicting a target value. In chemoinformatics context, the determination of the most significant set of descriptors is of great importance due to their contribution for improving ADMET prediction models. In this paper, an evolutionary-based approach for descriptor selection aimed to physicochemical property prediction is presented. In particular, we propose a genetic algorithm with a fitness function based on decision trees, which evaluates the relevance of a set of descriptors. Other fitness functions, based on multivariate regression models, were also tested. The performance of the genetic algorithm as a feature selection technique was assessed for predicting logP (octanol-water partition coefficient), using an ensemble of neural networks for the prediction task. The results showed that the evolutionary approach using decision trees is a promising technique for this bioinformatic application.

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An effective procedure for constructing a hierarchical text classification system

Yoon

Lee

2005

J. Am. Soc. Inf. Sci.

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In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with large collections of practical data. In this article, we introduce a new evaluation scheme for internal node classifiers, which can be used effectively to develop a hierarchical classification system. We also show that our method for constructing the hierarchical classification system is very effective, especially for the task of constructing classifiers applied to hierarchy tree with a lot of levels.

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On feature distributional clustering for text categorization

Cited by 195 publications

References 12 publications

A Survey of Text Clustering Algorithms

A Survey of Text Clustering Algorithms

An Evolutionary Approach for Feature Selection applied to ADMET Prediction

An effective procedure for constructing a hierarchical text classification system

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