Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
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Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for classification is not known a priori and may change as a consequence of external circumstances. Many traditional algorithms are unable to provide satisfactory performance while supporting the highly variable response times that exemplify such applications. In such contexts, anytime algorithms, which are amenable to trading time for accuracy, have been found to be exceptionally useful and constitute an area of increasing research activity. Previous techniques for improving anytime classification have generally been concerned with optimizing the probability of correctly classifying individual objects. However, as we shall see, serially optimizing the probability of correctly classifying individual objects K times, generally gives inferior results to batch optimizing the probability of correctly classifying K objects. In this work, we show that this simple observation can be exploited to improve overall classification performance by using an anytime framework to allocate resources among a set of objects buffered from a fast arriving stream. Our ideas are independent of object arrival behavior, and, perhaps unintuitively, even in data streams with constant arrival rates our technique exhibits a marked improvement in performance. The utility of our approach is demonstrated with extensive experimental evaluations conducted on a wide range of diverse datasets.