Time series clustering improves with fast and accurate feature extraction
CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm
Machine Learning
Summary
Time series data is everywhere, like heartbeats or stock prices, and grouping similar patterns together can help us understand them better. Many current methods either take too long or don't group well. The authors created a new way called CLUES-WEASEL that quickly turns time series into useful features, simplifies them, and then clusters them efficiently. Their tests show it works better and faster than other methods, and it can also work with different feature extractors. This makes it a helpful tool for analyzing time series data without needing extra guesses or clues.
time seriesclusteringunsupervised learningfeature extractionWEASEL algorithmprincipal component analysisk-means algorithmmachine learning
Authors
Johann Faouzi
Abstract
Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series clustering, which consists in identifying clusters among a set of time series in an unsupervised fashion. Most time series clustering algorithms suffer from the same balancing act: they trade clustering performance for faster runtimes or vice versa. We present a novel time series clustering algorithm that we call CLUES-WEASEL, which stands for CLustering with the UnsupervisEd Second version of Word ExtrAction for time SEries cLassification. CLUES-WEASEL extracts features using the unsupervised version of the transformation step of WEASEL 2.0, which is a time series classification algorithm, then reduces these features using principal component analysis, and finally performs clustering with the $k$-means algorithm using these reduced extracted features. Through extensive experiments, we prove that CLUES-WEASEL is significantly better than any other existing time series clustering algorithm while being (much) faster than any state-of-the-art one. We also show that the architecture of CLUES-WEASEL can work well with other time series feature extraction algorithms. Our findings highlight the relevance of CLUES-WEASEL for time series clustering.