On the quality of ART1 text clustering

Massey, Louis

doi:10.1016/s0893-6080(03)00088-1

Cited by 39 publications

(19 citation statements)

References 22 publications

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“…Contrary to document classification, text clustering operates without training: it aims at grouping documents based on the similarity of their content. Clustering is hence more autonomous and adaptable than classification, but is generally less reliable [8].…”

Section: Related Workmentioning

confidence: 99%

Autonomous and Adaptive Identification of Topics in Unstructured Text

Massey

2011

Knowlege-Based and Intelligent Information and Engineering Systems

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Abstract. Existing topic identification techniques must tackle an important problem: they depend on human intervention, thus incurring major preparation costs and lacking operational flexibility when facing novelty. To resolve this issue, we propose an adaptable and autonomous algorithm that discovers topics in unstructured text documents. The algorithm is based on principles that differ from existing natural language processing and artificial intelligence techniques. These principles involve the retrieval, activation and decay of general-purpose lexical knowledge, inspired by how the brain may process information when someone reads. The algorithm handles words sequentially in a single document, contrary to the usual corpus-based bag-of-words approach. Empirical results demonstrate the potential of the new algorithm.

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Section: Related Workmentioning

confidence: 99%

Autonomous and Adaptive Identification of Topics in Unstructured Text

Massey

2011

Knowlege-Based and Intelligent Information and Engineering Systems

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“…The ART is a method guided by a vigilance parameter, which helps to determine if the network represents a chosen sample sufficiently well. It has been used for clustering documents, e.g., the basic ART in [24] or Fuzzy ART [25]. Kohonen's SOM and its variants [26], allow clustering and visualization of documents and their relationships in a two-dimensional (2-D) representation.…”

Section: B Neural Network For Document Organizationmentioning

confidence: 99%

Web Content Management by Self-Organization

Freeman

Yin

2005

IEEE Trans. Neural Netw.

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Abstract-We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.

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“…The work presented here is very different from our previous work with ART (Massey (2002(Massey ( , 2003(Massey ( , 2005a where we tested a standard version of ART under various conditions of text clustering and measured the effectiveness of documents grouping by topics for real-life applications. Here, we present and analyse in detail the ART stabilisation problem we have previously identified briefly in Massey (2005b).…”

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confidence: 92%

“…ART networks properties of stability and plasticity as well as their ability to process dynamic data efficiently make them attractive candidates for recognizing patterns in large, rapidly changing data sets generated in real-life environments. The applications of ART span many domains, including among others sonar signal recognition (Carpenter and Streilein 1998), parts management at Boeing (Caudell et al 1991) and text clustering (Massey 2003).…”

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confidence: 99%

Conceptual duplication

Massey

2007

Soft Comput

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Stability and plasticity in learning systems are both equally essential, but achieving stability and plasticity simultaneously is difficult. Adaptive resonance theory (ART) neural networks are known for their plastic and stable learning of categories, hence providing an answer to the so called stability-plasticity dilemma. However, it has been demonstrated recently that contrary to general belief, ART stability is not possible with infinite streaming data. In this paper, we present an improved stabilization strategy for ART neural networks that does not suffer from this problem and that produces a soft-clustering solution as a positive side effect. Experimental results in a task of text clustering demonstrate that the new stabilization strategy works well, but with a slight loss in clustering quality compared to the traditional approach. For real-life intelligent applications in which infinite streaming data is generated, the stable and soft-clustering solution obtained with our approach more than outweighs the small loss in quality.

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On the quality of ART1 text clustering

Cited by 39 publications

References 22 publications

Autonomous and Adaptive Identification of Topics in Unstructured Text

Autonomous and Adaptive Identification of Topics in Unstructured Text

Web Content Management by Self-Organization

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