Fuzzy Adaptive Resonance Theory for Content-Based Data Retrieval

Fard, Amin Milani; Akbari, Hoda; Mohammad, R.; Akbarzadeh, T.

doi:10.1109/innovations.2006.301910

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The fuzzy association rules were then discovered from the questionnaire dataset to evaluate the performance of the proposed approach. Fard et al50 proposed a text and image retrieval architecture for processing dynamic Web content taxonomy using a fuzzy adaptive resonance theory neural network. This architecture handled the dynamic clustering of incremental information.…”

Section: Fuzzy Web Content Miningmentioning

confidence: 99%

A survey of fuzzy web mining

Lin

Hong

2013

WIREs Data Min & Knowl

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The Internet has become an unlimited resource of knowledge, and is thus widely used in many applications. Web mining plays an important role in discovering such knowledge. This mining can be roughly divided into three categories, including Web usage mining, Web content mining, and Web structure mining. Data and knowledge on the Web may, however, consist of imprecise, incomplete, and uncertain data. Because fuzzy‐set theory is often used to handle such data, several fuzzy Web‐mining techniques have been proposed to reveal fuzzy and linguistic knowledge. This paper reviews these techniques according to the three Web‐mining categories above—fuzzy Web usage mining, fuzzy Web content mining, and fuzzy Web structure mining. Some representative approaches in each category are introduced and compared. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Web Mining Technologies > Computational Intelligence

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Section: Fuzzy Web Content Miningmentioning

confidence: 99%

A survey of fuzzy web mining

Lin

Hong

2013

WIREs Data Min & Knowl

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“…Existing research shows that the web text noise may lower the performance of the data mining tasks on web text data [41], [42]. However, the ART-based systems have shown advantages on handling noisy web text and have been successfully used in many applications for web/text mining tasks [43], [44], [41], [45], [46]. In normal practices, the ART-based systems may handle noisy data through two ways:…”

Section: Handling Noisy Textmentioning

confidence: 99%

Learning Image-Text Associations

Jiang¹,

Tan

2009

IEEE Trans. Knowl. Data Eng.

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Web information fusion can be defined as the problem of collating and tracking information related to specific topics on the World Wide Web. Whereas most existing work on web information fusion has focused on text-based multidocument summarization, this paper concerns the topic of image and text association, a cornerstone of cross-media web information fusion. Specifically, we present two learning methods for discovering the underlying associations between images and texts based on small training data sets. The first method based on vague transformation measures the information similarity between the visual features and the textual features through a set of predefined domain-specific information categories. Another method uses a neural network to learn direct mapping between the visual and textual features by automatically and incrementally summarizing the associated features into a set of information templates. Despite their distinct approaches, our experimental results on a terrorist domain document set show that both methods are capable of learning associations between images and texts from a small training data set.

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Fuzzy Adaptive Resonance Theory for Content-Based Data Retrieval

Cited by 2 publications

References 17 publications

A survey of fuzzy web mining

A survey of fuzzy web mining

Learning Image-Text Associations

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