An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation

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.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Daniel Lemire, Martin Brooks, Yuhong Yan
• Keywords: Data Mining, ICDM 2005, Linear Time Algorithm, Significant Qualitative Characteristic, Top-down Regression Algorithm