Hierarchical Automatic Classification of News Articles based on Association Rules

Joo, Kil-Hong; Shin, Eun‐Young; Lee, Joo-Il; Lee, Won Suk

doi:10.9717/kmms.2011.14.6.730

Cited by 9 publications

(6 citation statements)

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“…It was initially proposed as the approach to the text categorization by Jo and Cho in 2008 [22]. It was utilized to the soft classification of texts which allows to assign more than one category to each text by Jo in 2008 [2]. It was improved into the more robust and stable approach to the text categorization by Jo in 2015 [5].…”

Section: Non-numerical Vector Based Clustering Algorithmsmentioning

confidence: 99%

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String Vector based AHC Algorithm for Clustering Words Semantically

2024

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This article proposes the modified AHC (Agglomerative HierarchicalClustering) algorithm which clusters string vectors, instead ofnumerical vectors, as the approach to the word clustering. Theresults from applying the string vector based algorithms to the textclustering were successful in previous works and synergy effectbetween the text clustering and the word clustering is expected bycombining them with each other; the two facts become motivations forthis research. In this research, we define the operation on stringvectors called semantic similarity, and modify the AHC algorithm byadopting the proposed similarity metric as the approach to the wordclustering. The proposed AHC algorithm is empirically validated asthe better approach in clustering words in news articles andopinions. We need to define and characterize mathematically moreoperations on string vectors for modifying more advanced machinelearning algorithms.

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Section: Non-numerical Vector Based Clustering Algorithmsmentioning

confidence: 99%

“…The rows and the columns of the matrix which is presented in Figure 4, correspond to texts in the corpus, and each element becomes the similarity between corresponding texts. A string vector is defined as an ordered finite set of strings as shown in equation (2),…”

Section: B Semantic Similarity Between String Vectorsmentioning

confidence: 99%

String Vector based AHC Algorithm for Clustering Words Semantically

2024

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“…It was initially proposed as a method of categorizing texts by Jo and Cho, in 2008 [22]. It was applied to fuzzy classification of texts which allows to assign more than one category to each text by Jo in 2008 [3]. It was upgraded into the more robust and stable approach to the text categorization by Jo in 2015 [6].…”

Section: Non-numerical Vector Based Clustering Algorithmsmentioning

confidence: 99%

Applying Table based AHC Algorithm to Semantic Word Clustering

2024

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This article proposes the modified AHC (Agglomerative HierarchicalClustering) algorithm which clusters tables, instead of numericalvectors, as the approach to the word clustering. The motivations ofthis research are the successful results from applying the tablebased algorithms to the text clustering tasks in previous works andthe expectation of synergy effect between the text clustering andthe word clustering. In this research, we define the similaritymetric between tables representing words, and modify the AHCalgorithm by adopting the proposed similarity metric as the approachto the word clustering. The proposed AHC algorithm is empiricallyvalidated as the better approach in clustering words in newsarticles and opinions. In using the table based AHC algorithm, it iseasier to trace results from clustering words.

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“…It was initially proposed as the approach to the text categorization by Jo and Cho in 2007 [3]. It was applied to the soft text categorization where each text is allowed to be classified into more than one category by Jo in 2008 [8]. It was improved and stabilized as the approach to both the hard text categorization and the soft text categorization in 2015 [16].…”

Section: Table Based Machine Learning Algorithmsmentioning

confidence: 99%

“…For each category, the number of nearest neighbors which belong it is counted as shown in equation (8),…”

Section: Novice Itemmentioning

confidence: 99%

Table based K Nearest Neighbor for Text Classification

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This article proposes the modified KNN (K Nearest Neighbor)algorithm which receives a table as its input data and is applied tothe text categorization. The motivations of this research are thesuccessful results from applying the table based algorithms to thetext categorizations in previous works and the expectation ofsynergy effect between the text categorization and the wordcategorization. In this research, we define the similarity metricbetween two tables representing texts, modify the KNN algorithm byreplacing the exiting similarity metric by the proposed one, andapply it to the text categorization. The proposed KNN is empiricallyvalidated as the better approach in categorizing texts in newsarticles and opinions. In using the table based KNN algorithm, it iseasier to trace results from categorizing texts.

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Hierarchical Automatic Classification of News Articles based on Association Rules

Cited by 9 publications

References 0 publications

String Vector based AHC Algorithm for Clustering Words Semantically

String Vector based AHC Algorithm for Clustering Words Semantically

Applying Table based AHC Algorithm to Semantic Word Clustering

Table based K Nearest Neighbor for Text Classification

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