An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation Daniel Lemire, Martin Brooks, and Yuhong Yan   View Paper (PDF)   Return to Session 3: Pattern Analysis on Text and Structured Data Abstract 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. ©2006 Association for Computing Machinery