(c) by Data Science London @ds_ldn
I have just attended a talk on copula-based clustering algorithms, where work of Francesca Marta Lilja Di Lascio and Simone Giannerini was presented.
Copula-based clustering is a kind of model-based clustering where each cluster is modeled as a set of realizations of one random variable. To model k clusters, k-dimensional copulas are used. Different kinds of copulas can be used for modelling the data; it’s up to the researcher to decide which ones to choose.
The method is particularly useful for datasets with a high dependence between different clusters. Its performance degrades when dependence is lower.
The R package CoClust, which implements a new clustering method based on copulas, is available on CRAN: http://cran.r-project.org/web/packages/CoClust/ (R Help)
References:
I have attended “Python for Data Analysis” meeting organised by Data Science London. There were two main talks — by Didrik Pinte from Enthought and by Wes McKinney, creator of pandas.
NumPy, the Python foundation for number crunching
by Didrik Pinte @dpinte — Python contributor to QuantLib (a library for quant finance), and MD of Enthought, developer of EPD-the scientific computing Python platform.
(c) by Data Science London @ds_ldn
About a half of the audience has already used NumPy, though I think only a couple of people has gone deep with C integration and memory optimizations. So it was a mix of an introductory talk with showing Cython code and profiling tools.
Interestingly, when someone decided to port NumPy to .NET, it didn’t work efficiently because of unpredictable garbage collection in .NET.
Didrik has also shown how a memory monitor from Pikos works.
Python for Data Analysis
by Wes McKinney @wesmckinn — former quant, author of pandas (the powerful Python library for data analysis), author of the book: “Python for Data Analysis”
(c) by Data Science London @ds_ldn
Most of the talk was done in the ipython notebook. Using a MovieLens dataset as an example, Wes has shown different pandas functions: data slicing, merge, map etc. The library is also good for data munging/cleaning/preparation.
He told they are doing further improvements of the library because of use cases when people try to open a 5 GB Kaggle dataset and the system uses 20 GB of memory.
Rmagic library: running R code in Python. Useful e.g. for ggplot2 library, which has no matches in the Python world.
“Python for Data Analysis” book is an introduction to pandas with working code examples, a better learning material than plain documentation. Print copies are not available yet; books will probably appear for Strata New York. (I have just checked my O’Reilly account, my copy is not listed as an “early release” anymore).
The first speaker, Didrik, added two reasons to use pandas:
Open Interests Europe Hackathon will take place in London, November 24 and 25.
Open Interests Europe brings together developers, designers, activists, journalists and other geeks for two days of learning, fun, intense hacking and app building.
How EU money is spent is an issue that concerns everyone who pays taxes to the EU. As the influence of Brussels lobbyists grows, it is increasingly important to draw the connections between lobbying, policy-making and funding. Journalists and activists need browsable databases, tools and platforms to investigate lobbyists’ influence and where the money goes in the EU. Join us and help build these tools!
The Hackathon challenges include Lobbying Transparency and Fish Subsidies.
Organised by the Open Knowledge Foundation and the European Journalism Centre, sponsored by Knight-Mozilla OpenNews.
For more details, please see the event website:
http://okfnlabs.org/events/hackdays/lobbying.htmland register at http://openinterests.eventbrite.com
Related links:
images © hitomi kai yoda
Japanese designer Yuri Suzuki has sent DesignBoom images of his ‘London underground circuit maps’ project, developed as part of the Designers in Residence program at the London Design Museum, on show until January 13th, 2013.
“The city of Palo Alto in California joined over a dozen cities around the United States and globe when it launched its own open data platform.
The city initially published open datasets that include the 2010 census data, pavement condition, city tree locations, park locations, bicycle paths and hiking trails, creek water level, rainfall and utility data. Open data about Palo Alto budgets, campaign finance, government salaries, regulations, licensing, or performance — which would all offer more insight into traditional metrics for government accountability — were not part of this first release.
The platform includes an application programming interface (API) which enables direct access through a RESTful interface to open government data published in a JSON format. Datasets can also be embedded like YouTube videos.”
— Palo Alto looks to use open data to embrace ‘city as a platform’, O’Reilly Radar
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