  For the past decades we have experienced an enormous expansion of the
accumulated data that humanity produces. Daily a numerous number of smart
devices, usually interconnected over internet, produce vast, real-values
datasets. Time series representing datasets from completely irrelevant domains
such as finance, weather, medical applications, traffic control etc. become
more and more crucial in human day life. Analyzing and clustering these time
series, or in general any kind of curves, could be critical for several human
activities. In the current paper, the new Curves Clustering Using Common
Patterns (3CP) methodology is introduced, which applies a repeated pattern
detection algorithm in order to cluster sequences according to their shape and
the similarities of common patterns between time series, data curves and
eventually any kind of discrete sequences. For this purpose, the Longest
Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has
been used in combination with the All Repeated Patterns Detection (ARPaD)
algorithm in order to perform highly accurate and efficient detection of
similarities among data curves that can be used for clustering purposes and
which also provides additional flexibility and features.
