Clustering of Short Strings in Large Databases

—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.

• Type: Conference
• Year: 2009
• Where: DEXAW
• Authors: Michail Kazimianec, Arturas Mazeika
• Keywords: Cluster Border, Database, DEXAW 2009, Short String Clustering, String Proximity Graph