TY - GEN
T1 - Shifting concepts to their associative concepts via bridges
AU - Zhai, Hongjie
AU - Haraguchi, Makoto
AU - Okubo, Yoshiaki
AU - Hashimoto, Kiyota
AU - Hirokawa, Sachio
PY - 2013
Y1 - 2013
N2 - This paper presents a pair of formal concept search procedures to find associative connection of concepts via bridge concepts. A bridge is a generalization of a sub-concept of an initial concept. The initial concept is then shifted to other target concepts which are conditionally similar to the initial one within the extent of bridge. A procedure for mining target concepts under the conditional similarity with respect to the bridge is presented based on an object-feature incident relation. Such a bridge concept is constructed in the concept lattice of person-feature incident relation. The latter incident relation is defined by aggregating the former document-feature relation to have more condensed relation, while keeping the variation of possible candidate bridges. Some heuristic rule, named Mediator Heuristics, is furthermore introduced to reflect user's interests and intention. The pair of these two procedures provides an efficient method for shifting initial concepts to target ones via some bridges. We show their usefulness by applying them to Twitter data.
AB - This paper presents a pair of formal concept search procedures to find associative connection of concepts via bridge concepts. A bridge is a generalization of a sub-concept of an initial concept. The initial concept is then shifted to other target concepts which are conditionally similar to the initial one within the extent of bridge. A procedure for mining target concepts under the conditional similarity with respect to the bridge is presented based on an object-feature incident relation. Such a bridge concept is constructed in the concept lattice of person-feature incident relation. The latter incident relation is defined by aggregating the former document-feature relation to have more condensed relation, while keeping the variation of possible candidate bridges. Some heuristic rule, named Mediator Heuristics, is furthermore introduced to reflect user's interests and intention. The pair of these two procedures provides an efficient method for shifting initial concepts to target ones via some bridges. We show their usefulness by applying them to Twitter data.
UR - http://www.scopus.com/inward/record.url?scp=84881242063&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-39712-7_45
DO - 10.1007/978-3-642-39712-7_45
M3 - Conference contribution
AN - SCOPUS:84881242063
SN - 9783642397110
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 586
EP - 600
BT - Machine Learning and Data Mining in Pattern Recognition - 9th International Conference, MLDM 2013, Proceedings
T2 - 9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013
Y2 - 19 July 2013 through 25 July 2013
ER -