Visualization Data Mining
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Visualization Data Mining
Interactive data mining techniques
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http://www.ted.com/talks/lang/en/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html#69000

http://www.ted.com/talks/lang/en/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html#69000
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Visualize complex data with subplots | (R news & tutorials)

Visualize complex data with subplots | (R news & tutorials) | Visualization Data Mining | Scoop.it
... for data. Yet I rarely see graphs used this way within visualizations. ... This approach is a go-to tool for understanding overplotted data, but it discards a lot of information. Since we're ... Visualize complex data with subplots ...
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Sense Networks | CrunchBase Profile

A machine-learning startup founded by Greg Skibiski and co-founded by MIT computer scientist Sandy Pentland, Sense Networks collects and analyzes...
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Aaron Koblin:人性的藝術表達 | Video on TED.com

Aaron Koblin:人性的藝術表達 | Video on TED.com | Visualization Data Mining | Scoop.it
TED Talks 藝術家Aaron Koblin收集了大量的數據,有時還包括有大量的人,並將其滙編成令人驚豔的視覺效果。從追蹤空中航班的優美線條到手機數據打造的起伏景色,從來自眾人的繪畫聚合而成的Johnny Cash視頻到為每一個用戶定制的“鬧市荒原”視頻,他的作品以精妙的方式探索著現代科技如何使我們的人性更加突出。...
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TED: Ruby Wax: What's so funny about mental illness? - Ruby Wax (2012)

Diseases of the body garner sympathy, says comedian Ruby Wax -- except those of the brain. Why is that? With dazzling energy and humor, Wax, diagnosed a decade ago with clinical depression, urges us to put an end to the stigma of mental illness.
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Social Network Analytics & Visualization « Next generation BI

Social Network Analytics & Visualization « Next generation BI | Visualization Data Mining | Scoop.it
Linkedin, Facebook & other social network uses the network information, to predict “People you may know” & offer people recommendations. Product ...
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Battle of the Charts - Just in time for our needs )

Battle of the Charts - Just in time for our needs ) | Visualization Data Mining | Scoop.it
Undeniably, radial charts based on a polar coordinate system are more visually appealing than regular line, area and bar charts, or even tree maps, all of which...

Via Siarhei Mardovich, Nikolai Chesalin
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Sense Networks: Location Data Simulation

The above animation depicts the process of identifying the primary tribe of the top 200 nightlife destinations in San Francisco during a certain period of the night:

1) Identify and isolate the top 200 nightlife destinations
2) Create a network of movement between these locations
3) Machine learning algorithms analyze each location in the context of the overall movement and categorize it (colored dots added) by examining everyone’s point of origin, and where everyone goes afterwards
4) The categorized places are now grouped by behavioral similarity, not proximity (convergent dots represent places with the most similar “type” of people present, in real-time, versus geographical proximity)
5) The spatial behavioral map is overlaid onto a spatial geographical map and the system continuously learns as new live data is received

 

Sense Networks attributes 487,500 dimensions to every place in a city, thus identifying a unique and complex “DNA” which describes it completely. The dimensions are based on the movement of people in and out of that place over time, and the places those people visit before and afterwards. Proprietary MVE (Minimum Volume Embedding) algorithms reduce the dimensionality of location and temporal data to 2 dimensions while retaining over 90% of the information. This allows for visualizations of data to better understand key dimensions and data relationships. (https://www.sensenetworks.com/products/macrosense-technology-platform/machine-learning/)

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Data Visualization, Design and Information Munging // Martin Krzywinski / Genome Sciences Center

Data Visualization, Design and Information Munging // Martin Krzywinski / Genome Sciences Center | Visualization Data Mining | Scoop.it

Circos is a software package for visualizing data and information.


Via Nikolai Chesalin
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A Taxonomy of Data Visualization

A Taxonomy of Data Visualization | Visualization Data Mining | Scoop.it

"For some time at Visualizing, we’ve been working on a commonsense taxonomy of data visualization. This is still a work in progress, but we wanted to involve the wider community in the discussion.

 

There are already all-inclusive glossaries of specific techniques, and there are several academic approaches to classification (Bertin, Schneiderman, etc.). But we're looking for something in between: a general, top-level language to describe the forms used in visualization and information graphics. It should be useful to experts and non-experts alike, and so requires a balance between familiar words and ideas on the one hand and rigorous thinking on the other."


Via Nikolai Chesalin
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