Time series clustering is an important task in its own right, and often a subroutine in other higher-level algorithms. However, clustering subsequences of a time series is known to be a particularly hard problem, and it has been shown that naive clustering of subsequences yields meaningless results under common assumptions. In this work, we introduce Time2Cluster, a novel representation and accompanying algorithm that meaningfully clusters time series subsequences. Our key insight is to avoid depending solely on relative distance information between subsequences, and instead to exploit information about the neighborhood subsequences. Our algorithm uses neighborhood information to mitigate the negative effects of small variations, such as phase shift, between the subsequences of time series data.