.png){kind=icon}
.png){kind=icon}
Return
Storage-optimizing clustering algorithms for high-dimensional tick data
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2013.12.046.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Tick data are used in several applications that need to keep track of values changing over time, like prices on the stock market or meteorological measurements. Due to the possibly very frequent changes, the size of tick data tends to increase rapidly. Therefore, it becomes of paramount importance to reduce the storage space of tick data while, at the same time, allowing queries to be executed efficiently. In this paper, we propose an approach to decompose the original tick data matrix by clustering their attributes using a new clustering algorithm called Storage-Optimizing Hierarchical Agglomerative Clustering (SOHAC). We additionally propose a method for speeding up SOHAC based on a new lower bounding technique that allows SOHAC to be applied to high-dimensional tick data. Our experimental evaluation shows that the proposed approach compares favorably to several baselines in terms of compression. Additionally, it can lead to significant speedup in terms of running time. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Tick data
Clustering
Storage
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W