Quantiles on Streams

Quantiles and Equi-depth Histograms over Streams

[7] A. Asuncion and D. J. Newman. UCI Machine Learning Repository

[3] Rakesh Agrawal and Arun Swami. A one-pass space-efficient algorithm for finding quantiles. A one-pass algorithm for accurately estimating quantiles for disk-resident data. [8] Jürgen Beringer and Eyke Hüllermeier. An efficient algorithm for instance-based learning on data streams.

Influences of Spatial and Temporal Variation on Fish–Habitat Relationships Defined by Regression Quantiles

—We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The spatial and temporal stability of model predi...

Evaluation of Summarization Schemes for Learning in Streams

Traditional discretization techniques for machine learning, from examples with continuous feature spaces, are not efficient when the data is in the form of a stream from an unknown, possibly changing, distribution. We present a time-and-memory-efficient discretization technique based on computing ε-approximate exponential frequency quantiles, and prove bounds on the worst-case error introduced ...

A nearly optimal and deterministic summary structure for update data streams

We present a deterministic summary structure over update streams that enables deterministic and the first space-optimal algorithms for a variety of problems, including, estimating frequencies, finding approximate frequent items, finding approximate quantiles, finding hierarchical heavy hitters, approximately optimal B-bucket histograms, estimating inner product sizes, etc..

Exponentially Weighted Simultaneous Estimation of Several Quantiles

In this paper we propose new method for simultaneous generating multiple quantiles corresponding to given probability levels from data streams and massive data sets. This method provides a basis for development of single-pass low-storage quantile estimation algorithms, which differ in complexity, storage requirement and accuracy. We demonstrate that such algorithms may perform well even for hea...