Stretchy Time Pattern Mining: A Deeper Analysis of Environment Sensor Data

Mining sequential patterns on environment sensor data is a challenging task; the data can present noises and may also contain sparse patterns, which are difficult to be detected. The knowledge extracted from environment sensor data can be used to determine climate changes. However, there is a lack of methods that can handle this kind of database. In this paper, we propose a method to mine sequential patterns in sparse, incomplete and noisy sensor data. The proposed method, called Stretchy Time Windows (STW), allows the mining of sequential patterns that present time gaps between their events. We propose an algorithm to implement STW, called Miner of Stretchy Time Sequences (MSTS). The proposed algorithm works with sequences of any size and uses a balanced strategy to analyze the search space. Our experiments show that MSTS returns sequences that have a longer period of analysis than GSP a traditional frequent pattern mining algorithm. In fact, 5 times larger than GSP and higher number of patterns (2.3 times) when compared to previous methods.