TY - GEN
T1 - Clustering complex multimedia objects using an ensemble approach
AU - Oviedo, Ana Isabel
AU - Ortega, Oscar
PY - 2012
Y1 - 2012
N2 - A complex multimedia object is an information unit composed by multiple media types like text, images, audio and video. Applications related with huge sets of such objects exceed the human capacity to synthesize useful information. The search for similarities and dissimilarities among objects is a task that has been done through clustering analysis, which tries to find groups in unlabeled data sets. Such analysis applied to complex multimedia object sets has a special restriction. The method must analyze the multiple media types present in the objects. This paper proposes a clustering ensemble that jointly assesses several media types present in this kind of objects. The proposed ensemble was applied to cluster webpages, constructing a text and image clustering prototypes. The Hubert's statistic was used to evaluate the ensemble performance, showing that the proposed method creates clustering structures more similar to the real classification than a joint-feature vector.
AB - A complex multimedia object is an information unit composed by multiple media types like text, images, audio and video. Applications related with huge sets of such objects exceed the human capacity to synthesize useful information. The search for similarities and dissimilarities among objects is a task that has been done through clustering analysis, which tries to find groups in unlabeled data sets. Such analysis applied to complex multimedia object sets has a special restriction. The method must analyze the multiple media types present in the objects. This paper proposes a clustering ensemble that jointly assesses several media types present in this kind of objects. The proposed ensemble was applied to cluster webpages, constructing a text and image clustering prototypes. The Hubert's statistic was used to evaluate the ensemble performance, showing that the proposed method creates clustering structures more similar to the real classification than a joint-feature vector.
KW - Clustering
KW - Complex multimedia objects
KW - Ensemble methods
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=84862190702&partnerID=8YFLogxK
M3 - Ponencia publicada en las memorias del evento con ISBN
AN - SCOPUS:84862190702
SN - 9789898425980
T3 - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
SP - 134
EP - 143
BT - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
T2 - 1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
Y2 - 6 February 2012 through 8 February 2012
ER -