The goal of this study is to mine meaningful patterns effectively and efficiently via change-point detection of the time series data, with the assistance of domain knowledge and observed data. With those patterns, our method can do segmentation and compression. We developed a novel gray-box approach for mining such data: Domain Assisted Parameter semi-free wave mining (DAPs). DAPs is intended for mining time series with rich domain-specific knowledge based on a chaos model. Specifically, it automatically detects a change-point of time sequences, respecting the minimal description length principle. And the time sequence is segmented based on the detected change-point, and each segment is fitted with a consistent model. The experimental results using both synthetic and real EEG data indicated that the developed method offers a significant improvement in segmentation and compression via pattern detection over other existing methods. DAPs reduced the number of bits of the observed data by detecting the changes in the patterns contained therein and brought about a higher average compression ratio, 1.6% more than WT (level 5). DAPs provides the advantages of (a) being capable of automatically detecting meaningful patterns, (b) being parameter semi-free, and (c) resulting in a huge reduction in data storage. These findings provide possible applications in the use of various medical devices that produce vast amounts of physiological data that should be monitored.