Semantic clustering of information systems' users with stochastic techniques

Raftopoulos, K; Papadakis, N; Ntalianis, K; Kollias, S

dc.contributor.author	Raftopoulos, K	en
dc.contributor.author	Papadakis, N	en
dc.contributor.author	Ntalianis, K	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:44:57Z
dc.date.available	2014-03-01T02:44:57Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32042
dc.subject	Markovian state clustering	en
dc.subject	Semantic clustering	en
dc.subject	User profile clustering	en
dc.subject.other	Chlorine compounds	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Flow interactions	en
dc.subject.other	Flow of solids	en
dc.subject.other	Information theory	en
dc.subject.other	Rough set theory	en
dc.subject.other	Semantics	en
dc.subject.other	State space methods	en
dc.subject.other	Disjoint subsets	en
dc.subject.other	Information access	en
dc.subject.other	Information systems	en
dc.subject.other	International conferences	en
dc.subject.other	Keyword space	en
dc.subject.other	Markovian	en
dc.subject.other	Markovian models	en
dc.subject.other	Markovian state clustering	en
dc.subject.other	Semantic clustering	en
dc.subject.other	Semantic distance	en
dc.subject.other	Semantic relevance	en
dc.subject.other	State spaces	en
dc.subject.other	Stochastic techniques	en
dc.subject.other	Strong interactions	en
dc.subject.other	User profile clustering	en
dc.subject.other	User profiling	en
dc.subject.other	Weak interactions	en
dc.subject.other	Industrial economics	en
dc.title	Semantic clustering of information systems' users with stochastic techniques	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICSC.2007.27	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICSC.2007.27	en
heal.identifier.secondary	4338391	en
heal.publicationDate	2007	en
heal.abstract	We use a Markovian model to capture the habitual user profiles of an information access system. In this model, the general as well as the individual for each user, profile is captured in the form of a Markovian process where the states are the keywords asked to the system by the users and a transition from state to state corresponds to the order theses keywords appeared in the queries. Under this model the probabilistic locality of the Markovian state space translates to semantical locality of the corresponding keywords in a way that a clustering of the Markovian state space corresponds to a semantic clustering of the keyword space. Since the states represent keywords asked by the users, the state space can grow very large, but at the same time it is partitioned into disjoint subsets such that strong interactions among the states of the same subset exists but weak interactions among states of different subsets. We exploit this structure to effectively cluster the large state space and reveal the corresponding semantic keyword clusters. We then define a semantic distance between the various user profiles that can be used to cluster the user space on the basis of keyword usage and keyword semantic relevance. The resulting clustering achieves high independence from the row data. Users for e.g. that never asked a common keyword may end up very close to each other if their keywords were asked together by many other users. © 2007 IEEE.	en
heal.journalName	ICSC 2007 International Conference on Semantic Computing	en
dc.identifier.doi	10.1109/ICSC.2007.27	en
dc.identifier.spage	535	en
dc.identifier.epage	542	en