<mods:mods version="3.0" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-0.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mods="http://www.loc.gov/mods/v3"><mods:titleInfo><mods:title>Query Clustering using Click-Through Graph</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Jeonghee</mods:namePart><mods:namePart type="family">Yi</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Farzin</mods:namePart><mods:namePart type="family">Maghoul</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>In this p aper w e describe a problem of d iscovering query clusters from a click -through graph of w eb search logs. The graph consists of a set of web search queries, a set of pag es selected for the queries, and a set of d irected edges that connects a query node and a page node click ed by a user for the query. The proposed method extracts all m axim al b ipartite cliques (b icliques) from a click-through graph and compute an equiv alence set of queries (i.e., a query cluster) from the m axim al bicliques. A cluster of queries is form ed from th e queries in a biclique. We present a scalable algorithm that enumerates all maximal bicliques from the click-through graph. We h ave conducted experim ents on Yahoo web search queries and the result is p romising.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8061">2009-04</mods:dateIssued></mods:originInfo><mods:genre>Conference or Workshop Item</mods:genre></mods:mods>