Web Content Management by Self-Organization

Freeman, Richard; Yin, Hujun

doi:10.1109/tnn.2005.853415

Cited by 16 publications

(6 citation statements)

References 42 publications

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“…An approach latent semantic indexing of 1D-Self-Organizing Map can be used to reduce the dimensionality of document vectors without essentially losing information contained in the full vocabulary. Richard T.Freeman et al [29] presents a method for content management and knowledge discovery using a topology-preserving neural network and this method provides a natural way to automatically generate topic directories and structures for efficient information access and retrieval.…”

Section: Related Workmentioning

confidence: 99%

An Appropriate Feature Classification Model Using Kohonen Network

Sridevi¹,

Dinadayalan²,

Britto³

2019

IJCET

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Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. "An Appropriate Feature Classification Model using Kohonen Network (AFCM)" is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.

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Section: Related Workmentioning

confidence: 99%

An Appropriate Feature Classification Model Using Kohonen Network

Sridevi¹,

Dinadayalan²,

Britto³

2019

IJCET

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“…These can be assimilated to the input references, and the crawler engine uses them to refine the representation of target concepts [18,24]. Clustering seems the most appropriate technology to synthesize an aggregate conceptual scenario from a set of positive examples [17,[25][26][27]. The grouping algorithm takes into account the semantic descriptions of samples rather than their lexical distributions.…”

Section: A Positive Feedback Handling: Semantic Clusteringmentioning

confidence: 99%

An analyst-adaptive approach to focused crawlers

Zunino

Bisio

Peretti

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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The paper presents a general methodology to implement a flexible Focused Crawler for investigation purposes, monitoring, and Open Source Intelligence (OSINT). The resulting tool is specifically aimed to fit the operational requirements of law-enforcement agencies and intelligence analyst. The architecture of the semantic Focused Crawler features static flexibility in the definition of desired concepts, used metrics, and crawling strategy; in addition, the method is capable to learn (and adapt to) the analyst's expectations at runtime . The user may instruct the crawler with a binary feedback (yes/no) about the current performance of the surfing process, and the crawling engine progressively refines the expected targets accordingly. The method implementation is based on an existing text-mining environment, integrated with semantic networks and ontologies. Experimental results witness the effectiveness of the adaptive mechanism.

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“…The TOC method [1] is a set of independently spanned and hierarchically organized 1-D growing SOMs. The TOC uses an entropy-based Bayesian information criterion (BIC) to determine the optimum number of nodes for each GC.…”

Section: Self Organising Mapsmentioning

confidence: 99%

“…The classification methods used are the backpropagation neural networks [6], naïve Bayesian [3] or support vector machine [19]. The applications include e-mail filtering and categorizing documents [1].…”

Section: Introductionmentioning

confidence: 99%

Self-organising map for document categorization using latent semantic analysis

Mahalakshmi

Duraiswamy

2010

2010 International Conference on Innovative Computing Technologies (ICICT)

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With the increasing amount of unstructured content available electronically on the web, content categorization becomes very important for efficient information retrieval. The basic approaches for information retrieval in text documents are searching using keywords, categorization of the documents and filtering out the stream. To extract information from raw data, its complexity needed to be first reduced. Clustering methods and Projection methods are aimed at reducing the amount of data and dimensionality of data respectively. SOM is a special case in that it can be used at the same time for both clustering and projection. It projects onto a 2D-grid. Various methods were developed for the automatic clustering of worldwide webdocuments according to the user requirements. The objective of this paper is to reduce the time and effort the user has to find the information sought after. The method termed topological organization of content can generate classified topics from a set of unstructured documents. The TOC is a set of hierarchically organized 1D-growing SOMs. In TOC, vector space model is used for indexing of 1D-SOM. In the proposed approach, latent semantic indexing of 1D-SOM can be used to enhance the association between terms. Latent semantic analysis is a technique that projects the original high dimensional document vector into a space with latent semantic dimensions. A term-bydocument matrix is constructed for the information retrieval. A brief review is given on existing methods for documents clustering and organization. The proposed method which can use LSI will be efficient in terms of computational cost, accuracy and visualization. It can be easily adapted for large data set. The proposed method will provide feature for retrieving meaningful related topics.Keywords-Document categorization, information retrieval, onedimensional self-organizing map (1D-SOM), latent semantic analysis(LSA).

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Web Content Management by Self-Organization

Cited by 16 publications

References 42 publications

An Appropriate Feature Classification Model Using Kohonen Network

An Appropriate Feature Classification Model Using Kohonen Network

An analyst-adaptive approach to focused crawlers

Self-organising map for document categorization using latent semantic analysis

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