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102

DEXAW
2009
IEEE

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Clustering of Short Strings in Large Databases

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Clustering of Short Strings in Large Databases

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—A novel method CLOSS intended for textual databases is proposed. It successfully identiﬁes misspelled string clusters, even if the cluster border is not prominent. The method uses q-gram approach to represent data and a string proximity graph to ﬁnd the cluster. Contribution refers to short string clustering in text mining, when the proximity graph has multiple horizontal lines or the line is not present.

Michail Kazimianec, Arturas Mazeika

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Cluster Border | Database | DEXAW 2009 | Short String Clustering | String Proximity Graph |

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Added	20 May 2010
Updated	20 May 2010
Type	Conference
Year	2009
Where	DEXAW
Authors	Michail Kazimianec, Arturas Mazeika

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