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ABSTRACT: Probability-based text clustering algorithm by alternately repeating two operations

Abstract

Owing to the rapid advance of internet technology, users have to face to a large amount of raw data from the World Wide Web every day, most of which is displayed in text format. This situation brings a great demand for efficient text analysis techniques by internet users. Since clustering is unsupervised and requires no prior knowledge, it is extensively adopted to help analyse textual data. Unfortunately, as far as I know, almost all the clustering algorithms proposed so far fail to deal with large-scale text collection. For precisely classifying large-scale text collection, a novel probability based text clustering algorithm by alternately repeating two operations (abbreviated as PTCART) is proposed in this paper. This algorithm just repeats two operations of (a) feature set construction and (b) text partition until the optimal partition is reached. Its convergent capacity is also validated. Experiments results demonstrate that, compared with several popular text clustering algorithms, PTCART has excellent performance.

via Probability-based text clustering algorithm by alternately repeating two operations.

Brian S McGowan, PhD

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Brian is a research scientist and educational technologist. He helped transform Pfizer’s Medical Education Group and previously served in educational leadership roles at HealthAnswers, Inc.; Acumentis, LLC.; Cephalon; and Wyeth. He taught graduate medical education programs at Arcadia University for 10 years. Dr. McGowan recently authored the book "#socialQI: Simple Solutions for Improving Your Healthcare" and has been invited to speak internationally on the subject of information flow, technology, and learning in healthcare.

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