  Time series clustering is the process of grouping time series with respect to
their similarity or characteristics. Previous approaches usually combine a
specific distance measure for time series and a standard clustering method.
However, these approaches do not take the similarity of the different
subsequences of each time series into account, which can be used to better
compare the time series objects of the dataset. In this paper, we propose a
novel technique of time series clustering based on two clustering stages. In a
first step, a least squares polynomial segmentation procedure is applied to
each time series, which is based on a growing window technique that returns
different-length segments. Then, all the segments are projected into same
dimensional space, based on the coefficients of the model that approximates the
segment and a set of statistical features. After mapping, a first hierarchical
clustering phase is applied to all mapped segments, returning groups of
segments for each time series. These clusters are used to represent all time
series in the same dimensional space, after defining another specific mapping
process. In a second and final clustering stage, all the time series objects
are grouped. We consider internal clustering quality to automatically adjust
the main parameter of the algorithm, which is an error threshold for the
segmenta- tion. The results obtained on 84 datasets from the UCR Time Series
Classification Archive have been compared against two state-of-the-art methods,
showing that the performance of this methodology is very promising.
