Multiscale approximation methods (MAME) to locate embedded consecutive subsequences—its applications in statistical data mining and spatial statistics

• Published in: Computers & industrial engineering Vol. 43; no. 4; pp. 703 - 720
• Main Author: Huo, Xiaoming
• Format: Journal Article
• Language: English
• Published: Seoul Elsevier Ltd 01.09.2002; Oxford Pergamon Press; New York, NY Pergamon Press Inc
• Subjects: Applied sciences; Approximation; Computer science; control theory; systems; Data mining; Estimating techniques; Exact sciences and technology; Information systems. Data bases; Maximum likelihood estimate; Memory organisation. Data processing; Multiscale approximation; Searches; Software; Studies; Multiscale approximation; Data mining; Maximum likelihood estimate; Data analysis; Statistical analysis; Multiscale method; Classification; Spatial analysis; Information extraction; Maximum likelihood; Search algorithm

In statistical data mining and spatial statistics, many problems (such as detection and clustering) can be formulated as optimization problems whose objective functions are functions of consecutive subsequences. Some examples are (1) searching for a high activity region in a Bernoulli sequence, (2)...