Data Curves Clustering Using Common Patterns Detection
This addresses the need for analyzing and clustering vast real-valued datasets from domains like finance and medicine, though it appears incremental as it builds on existing pattern detection techniques.
The paper tackles the problem of clustering time series and data curves by introducing the 3CP methodology, which uses pattern detection algorithms to identify similarities in shape and common patterns between sequences, achieving highly accurate and efficient clustering.
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.