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The author present novel algorithms for feature extraction and change detection in unstructured data, primarily in textual and sequential data. Keyword and feature extraction is a fundamental problem in text data mining and document processing. A majority of document processing applications directly depend on the quality and speed of keyword extraction algorithms. In this paper, a novel approach to rapid change detection in data streams and documents is developed. It is based on ideas from image processing and especially on the Helmholtz Principle from the Gestalt Theory of human perception. Applied to the problem of keywords extraction, it delivers fast and effective tools to identify meaningful keywords using parameter-free methods.
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