Документ взят из кэша поисковой машины. Адрес оригинального документа : http://hea-www.harvard.edu/AstroStat/slog/groundtruth.info/AstroStat/slog/2008/language-barrier/index.html
Дата изменения: Unknown
Дата индексирования: Sat Mar 1 14:26:17 2014
Кодировка:

Поисковые слова: п п п п п п п п р п р п р п р п р п р п р п р п р п р п
The AstroStat Slog » Blog Archive » language barrier

language barrier

Last week, I was at Tufts colloquium and happened to have a conversation with a computer scientist about density based clustering. I understood density as probabilistic density and was recollecting a paper by Fraley and Raftery (Model-Based Clustering, Discriminant Analysis, and Density Estimation, JASA, 2002, 97, p.458) and other similar papers I saw in engineering journals like IEEE transactions. For a few moments, I felt uncomfortable and she explained that density meant “how dense observations are.” Density based clustering was meant to be distance based clustering, like k-means, minimum spanning tree, most likely nonparametric approaches.

Although words are same, the first impression and their usage is quite different from society to society (even among statisticians). One word I’m very reluctant to use both to astronomers and statisticians is model. I’m quite confused at the reactions from both sides. To clarify meanings, implications, or intentions, some clever adjectives must accompany these common words; however, once one gets used to these jargons, adjectives are felt redundant to your fellow scientists/colleagues, whereas the other gets lost and seeks explanation of the usage by related examples and backgrounds.

Not only simple words, like model and density, there are more jargons requires inter-disciplinary semantic experts. Yet, patience of explaining and open-mindedness would easily assist to get over language barriers in any interdisciplinary works.

[ Would you mind sharing your experience of any language barrier? ]

One Comment
  1. TomLoredo:

    Hyunsook, good points. Although not the main topic of your post, your comment on the difference between density-based and distance-based clustering brings to mind the following tech report by Ian Davidson at SUNY Albany: Understanding K-Means Non-hierarchical Clustering. This is a short but insightful and readable discussion of some of the limitations of k-means clustering that every astronomer considering using k-means should read. Section 4.9 mentions the difference between density-based and distance-based clustering that Hyunsook alluded to. The Conclusion also briefly touches on the “model vs. truth” issue that is the topic of another recent Slog post by Hyunsook.

    02-20-2008, 3:07 pm
Leave a comment