id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_7qkqtvrpezcrtoaq3oc7zpsvg4	Krisztian Buza	Storage-optimizing clustering algorithms for high-dimensional tick data	2014	26	.pdf	application/pdf	8535	606	66	Storage-Optimizing Clustering Algorithms for High-Dimensional Tick Data our approach is based on the decomposition of a large tick data matrix into a number of smaller new clustering algorithm that minimize storage space required for a tick data matrix. approach for the decomposition of tick data matrices is based on clustering. storage space and therefore, as shown in large number of extensive experiments, SOHAC substantially outperforms conventional clustering algorithms for the tick data storage problem. clustering algorithm, SOHAC, that supports efficient storage of tick data. With decomposition of a tick data matrix M we mean the clustering of the regular columns of the problem defined in the previous section is to cluster the columns of a tick data matrix. Algorithm 1 SOHAC: Storage-Optimizing Hierarchical Agglomerative Clustering for Tick Data the clusters produced by our approach, SOHAC, to the decomposition of the same tick data matrix	./cache/work_7qkqtvrpezcrtoaq3oc7zpsvg4.pdf	./txt/work_7qkqtvrpezcrtoaq3oc7zpsvg4.txt