InforLorV4, Main, Exploration, bibRecord, 002786

A New Label Maximization Based Incremental Neural Clustering Approach: Application to Text Clustering

Identifieur interne : 002786 ( Main/Exploration ); précédent : 002785; suivant : 002787

A New Label Maximization Based Incremental Neural Clustering Approach: Application to Text Clustering

Auteurs : Jean-Charles Lamirel [France] ; Raghvendra Mall [Inde] ; Shadi Al Shehabi ; Ghada Safi

Source :

Lecture Notes in Computer Science [ 0302-9743 ]

RBID : ISTEX:66EFA7567D07A66E08F6541D90A5AA61F40F9C50

Abstract

Abstract: Neural clustering algorithms show high performance in the general context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental growing neural gas algorithm (IGNG) and the label maximization based incremental growing neural gas algorithm (IGNG-F). In this paper we highlight that there is a drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. Specific quality measures and cluster labeling techniques that are independent of the clustering method are used for the precise performance evaluation. We provide variations to incremental growing neural gas algorithm exploiting in an incremental way knowledge from clusters about their current labeling along with cluster distance measure data. This solution leads to significant gain in performance for all types of datasets, especially for the clustering of complex heterogeneous textual data.

Url:

https://api.istex.fr/ark:/67375/HCB-MQLX3TR0-8/fulltext.pdf

DOI: 10.1007/978-3-642-21566-7_26

Affiliations:

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to stream Istex, to step Corpus: 001799
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Le document en format XML

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A New Label Maximization Based Incremental Neural Clustering Approach: Application to Text Clustering

A New Label Maximization Based Incremental Neural Clustering Approach: Application to Text Clustering

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Abstract

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