A SOM-Based Document Clustering Using Frequent Max Substrings for Non-Segmented Texts

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Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
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"A SOM-Based Document Clustering Using Frequent Max Substrings for Non-Segmented Texts"
written by Todsanai Chumwatana, Kok Wai Wong, Hong Xie,
published by Journal of Intelligent Learning Systems and Applications, Vol.2 No.3, 2010

has been cited by the following article(s):

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CrossRef

[1]	The Classification of the Documents Based on Word2Vec and 2-Layer Self Organizing Maps
	International Journal of Machine Learning and Computing, 2018

[2]	Document Clustering using Self-Organizing Maps
	2017

[3]	Text document clustering using self organizing map: Theses and dissertations of universitas Indonesia
	2017

[4]	Snap-drift neural computing for intelligent diagnostic feedback.
	2017

[5]	Snap-drift neural computing for intelligent diagnostic feedback
	2017

[6]	Challenging Issues and Similarity Measures for Web Document Clustering
	2015

[7]	On the influence of training data quality on text document classification using machine learning methods
	International Journal of Knowledge Engineering and Data Mining, 2015

[8]	An investigation of Computational Intelligence Techniques And Their Application
	2015

[9]	Dimensionality reduction in text classification using scatter method
	International Journal of Data Mining, Modelling and Management, 2014

[10]	A SURVEY OF DOCUMENT CLUSTERING TECHNIQUES FOR NON-SEGMENTED LANGUAGES
	T Chumwatana - ird.sut.ac.th, 2014

[11]	Hybrid Method: Integration of the Frequent Max Substring Technique and Thai Language-Dependent Technique for Indexing Thai Text Documents
	T Chumwatana, KW Wong, 2014

[12]	On text document classification and retrieval using self-organising maps
	2014

[13]	Paired Indices for Clustering Evaluation
	2014

[14]	Clustering documents with maximal substrings
	Enterprise Information Systems, 2012

[15]	Self-Organising maps in document classification: A comparison with six machine learning methods
	Adaptive and Natural Computing Algorithms. Springer Berlin Heidelberg, 2011

[16]	DOCUMENTS AS A BAG OF MAXIMAL SUBSTRINGS-An Unsupervised Feature Extraction for Document Clustering
	2011

[17]	DOCUMENTS AS A BAG OF MAXIMAL SUBSTRINGS
	2011

[18]	A comparative study of clustering techniques for non-segmented language documents
	2011

[1]	Text document clustering using self organizing map: Theses and dissertations of universitas Indonesia 2017 3rd International Conference on Science in Information Technology (ICSITech), 2017 DOI:10.1109/ICSITech.2017.8257096

[2]	Enterprise Information Systems Lecture Notes in Business Information Processing, 2012 DOI:10.1007/978-3-642-29958-2_2

[3]	Adaptive and Natural Computing Algorithms Lecture Notes in Computer Science, 2011 DOI:10.1007/978-3-642-20282-7_27

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