d3.js. Information Visualization Manifesto. Ed. note: This article originally appeared on Manuel Lima's VC blog, and is reprinted here under the Creative Commons Attribution 3.0 Unported License. "The purpose of visualization is insight, not pictures" - Ben Shneiderman (1999) Over the past few months I've been talking with many people passionate about Information Visualization who share a sense of saturation over a growing number of frivolous projects. The criticism is slightly different from person to person, but it usually goes along these lines: "It's just visualization for the sake of visualization", "It's just eye-candy", "They all look the same". When Martin Wattenberg and Fernanda Viégas wrote about Vernacular Visualization, in their excellent article on the July-August 2008 edition of interactions magazine, they observed how the last couple of years have witnessed the tipping point of a field that used to be locked away in its academic vault, far from the public eye.
Form Follows Function Form doesn't follow data. Protovis. Protovis composes custom views of data with simple marks such as bars and dots. Unlike low-level graphics libraries that quickly become tedious for visualization, Protovis defines marks through dynamic properties that encode data, allowing inheritance, scales and layouts to simplify construction. Protovis is free and open-source, provided under the BSD License. It uses JavaScript and SVG for web-native visualizations; no plugin required (though you will need a modern web browser)! Although programming experience is helpful, Protovis is mostly declarative and designed to be learned by example. Protovis is no longer under active development.The final release of Protovis was v3.3.1 (4.7 MB). This project was led by Mike Bostock and Jeff Heer of the Stanford Visualization Group, with significant help from Vadim Ogievetsky.
Updates June 28, 2011 - Protovis is no longer under active development. September 17, 2010 - Release 3.3 is available on GitHub. May 28, 2010 - ZOMG! Getting Started. A visual exploration on mapping complex networks. Information Is Beautiful | Ideas, issues, knowledge, data - visualized! Information Visualization Database. 1991 computer graphics (XGvis) by Andreas Buja, Deborah F.
Swayne, Michael L. Littman, Nathaniel Dean From 1991 to 1996, there was a spate of development and public distribution of highly interactive systems for data analysis and visualization, e.g., XGobi, ViSta by Deborah Swayne, Di Cook, Andreas Buja, and Forrest Young (1940-2006). Buja, A., Asimov, D., Hurley, C., and McDonald, J. A. (1988). Swayne, D. Buja, A., Cook, D., and Swayne, D. Swayne, D. Young, F. Google Ngram Viewer. CiteSpace: visualizing patterns and trends in scientific literature.
CiteSpace is a freely available Java application for visualizing and analyzing trends and patterns in scientific literature. It is designed as a tool for progressive knowledge domain visualization (Chen, 2004). It focuses on finding critical points in the development of a field or a domain, especially intellectual turning points and pivotal points. Detailed case studies are given in (Chen, 2006) and other publications. CiteSpace provides various functions to facilitate the understanding and interpretation of network patterns and historical patterns, including identifying the fast-growth topical areas, finding citation hotspots in the land of publications, decomposing a network into clusters, automatic labeling clusters with terms from citing articles, geospatial patterns of collaboration, and unique areas of international collaboration.
The primary source of input data for CiteSpace is the Web of Science. Notes: You need to have Java Runtime installed on your computer. Yes. Disclaimer.
Data Mining Software. Заглавная страница. Data mining. Data Mining (рус. добыча данных, интеллектуальный анализ данных, глубинный анализ данных) — собирательное название, используемое для обозначения совокупности методов обнаружения в данных ранее неизвестных, нетривиальных, практически полезных и доступных интерпретации знаний, необходимых для принятия решений в различных сферах человеческой деятельности. Термин введён Григорием Пятецким-Шапиро в 1989 году[1][2][3]. Английское словосочетание «Data Mining» пока не имеет устоявшегося перевода на русский язык. При передаче на русском языке используются следующие словосочетания[4]: просев информации, добыча данных, извлечение данных, а, также, интеллектуальный анализ данных[5][6][7]. Более полным и точным является словосочетание «обнаружение знаний в базах данных» (англ. knowledge discovering in databases, KDD).
Введение[править | править исходный текст] Исторический экскурс[править | править исходный текст] Постановка задачи[править | править исходный текст] Что означает «скрытые знания»? Data Mining Map.