From my experience, these resources are worth read:

[1] Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop

Andreas Brandmaier's permutation distribution clustering is a method rooted in the dissimilarities between time series, formalized as the divergence between their permutation distributions. Personally, I think this is your "best" option http://cran.r-project.org/web/packages/pdc/index.html

Eamonn Keogh's SAX (Symbolic Aggregate Approximation) and iSAX routines develop "shape clustering" for time series

http://www.cs.ucr.edu/~eamonn/SAX.htm

There are approaches based on text compression algorithms that remove the redundancy in a sequence of characters (or numbers), creating a kind of distance or density metric that can be used as inputs to clustering, see, e.g.:

http://link.springer.com/chapter/10.1007/978-0-387-84816-7_4

This paper by Rob Hyndman Dimension Reduction for Clustering Time Series Using Global Characteristics, discusses compressing a time series down to a small set of global moments or metrics and clustering on those:

http://www.robjhyndman.com/papers/wang2.pdf

Chapter 15 in Aggarwal and Reddy's excellent book, Data Clustering, is devoted to a wide range (a laundry list, really) of time-series clustering methods (pps 357-380). The discussion provides excellent background to many of the issues specific to clustering a time series"

http://users.eecs.northwestern.edu/~goce/SomePubs/Similarity...

...and a lot more.

-- URL --

[1] https://www.amazon.com/Pattern-Recognition-Learning-Informat...