Abstract
The ubiquity and cheapness of miniature low-power sensors, digital processing, and large amounts of storage contained in small packages has heralded the ability to acquire large amounts of data about systems during their course of operation. The size and complexity of the data sets so generated have colloquially been labeled "big data." The computer science field of "data mining" has arisen with the purpose of extracting meaning from such data, expressly looking for patterns that not only link historic observations but also predict future behavior. This overview article considers the process, techniques, and interpretation of data mining, with specific focus on its application in audiology. Modern hearing instruments contain data-logging technology to record data separate from the audio stream, such as the acoustic environments in which the device was being used and how the signal processing was consequently operating. Combined with details about the patient, such as the audiogram, the variety of data generated lends itself to a data mining approach. To date, reports of the use and interpretation of these data have been mostly constrained to questions such as looking for changes in patterns of daily use, or the degree and direction of volume control manipulation as the patient's experience with a hearing aid changes. In this, and an accompanying results paper, the practical applications of some data mining techniques are described as applied to a large data set of examples of real-world device usage, as supplied by a hearing aid manufacturer.