An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation [r-libre/212]

Lemire, Daniel, Brooks, Martin et Yan, Yuhong (2005). An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation. Dans Han, Jiawei; Wah, Benjamin W.; Vijay, Raghavan; Wu, Xindong et Rastogi, Rajeev (dir.), Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05) (p. 709-712). Piscataway, NJ : IEEE. 10.1109/ICDM.2005.25

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem, present an optimal linear time algorithm based on novel formalism, and compare experimentally its performance to a linear time top-down regression algorithm. We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.
Déposant:	Lemire, Daniel
Responsable :	Daniel Lemire
Dépôt :	05 juin 2007
Dernière modification :	16 juill. 2015 00:47

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