Storage-optimizing clustering algorithms for high-dimensional tick data

Highlights：

• We investigate tick data for applications that keep track of values changing rapidly over time, like stock market prices.

• We decompose tick data by clustering its attributes using Storage-Optimizing Hierarchical Agglomerative Clustering.

• We propose a method for speeding up the algorithm based on a new lower bounding technique.

• Experimental results show that the proposed approach compares favorably to several baselines in terms of compression.

• Additionally, the proposed approach can lead to significant speedup in terms of running time.