C4.5: programs for machine learning
C4.5: programs for machine learning
Visualizing a discipline: an author co-citation analysis of information science, 1972–1995
Journal of the American Society for Information Science
Visualizing science by citation mapping
Journal of the American Society for Information Science
Mapping Scientific Frontiers: The Quest for Knowledge Visualization
Mapping Scientific Frontiers: The Quest for Knowledge Visualization
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking dynamics of topic trends using a finite mixture model
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Worldwide use and impact of the NASA Astrophysics Data System digital library: Research Articles
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Information Processing and Management: an International Journal - Special issue: Informetrics
Journal of the American Society for Information Science and Technology
Scientometrics
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Identifying the significance of specific concepts in the diffusion of scientific knowledge is a challenging issue concerning many theoretical and practical areas. We introduce an innovative visual analytic approach to integrate microscopic and macroscopic perspectives of a rapidly growing scientific knowledge domain. Specifically, our approach focuses on statistically unexpected phrases extracted from unstructured text of titles and abstracts at the microscopic level in association with the magnitude and timeliness of their citation impact at the macroscopic level. The H-index, originally defined to measure individual scientists. productivity in terms of their citation profiles, is extended in two ways: 1) to papers and terms as a means of dividing these items into two groups so as to replace the less optimal threshold-based divisions, and 2) to take into account the timeliness of the impact of knowledge diffusion in terms of the timing of citations and publications so that attention is particularly drawn towards potentially significant and timely papers. The selected terms are connected to higher-level performance indicators, such as measures derived from the H-index, in the form of decision trees. A top-down traversal of such decision trees provides an intuitive walkthrough of concepts and phrases that may underline potentially significant but currently still latent scientific discoveries. Timeliness measures can also help to identify institutions that are at the forefront of a research field. We illustrate how widely accessible tools such as Google Earth can be utilized to disseminate such insights. The practical significance for digital libraries and fostering scientific discoveries is demonstrated through the astronomical literature related to the Sloan Digital Sky Survey (SDSS).