» news

RCOMM 2010: Rapid Miner Conference

Vikramaditya Jakkula — Sat, 17 Jul 2010 03:14:24 +0000

RapidMiner Community Meeting And Conference – RCOMM 2010

As RapidMiner has once again proved to be the most-used open source data mining tool among the community of data analysts world-wide in a recent poll, it is now the time to give a face to that community. Therefore, Rapid-I hosts the first RapidMiner Community Meeting And Conference (RCOMM 2010) and invites users and developers of RapidMiner to take part and share their RapidMiner experiences with other members of the community. The RCOMM 2010 intends to intensify the community life and strengthen the RapidMiner network by bringing together users and developers of RapidMiner from all backgrounds, may they be scientific or commercial, from the whole variety of applications and from all grades of knowledge. A vital exchange of ideas, application reports, and scientific results will help beginners to advance and will inspire the already advanced leading them to professionalism. Users will profit from in-depth knowledge of developers, who in turn will gain from picking up requirements and ideas for further development.

The RCOMM 2010 encompasses conference talks, in which invited lecturers will discuss aspects of state-of-the-art data mining with RapidMiner. A Call-for-Papers will be issued for those who would like to present their work in that scope. Workshops will be held to give participants a hands-on experience concerning several topics regarding RapidMiner usage. Additionally, attendees of the RCOMM 2010 will also have the option to participate in several courses given by professional RapidMiner consultants in the surrounding of the user meeting.

Dates & Deadlines:

Submission Deadline:August 6, 2010

Notification of Acceptance:August 13, 2010

Camera-ready Papers:August 20, 2010

Conference: September 13 – 16, 2010

Visit Conference Home: RCOMM
Registration Link: Registration
Schedule Link: Schedule

DARPA pushing machine learning into electronic warfare

Vikramaditya Jakkula — Wed, 14 Jul 2010 07:17:41 +0000

Behavioral Learning for Adaptive Electronic Warfare (BLADE) program under DARPA is developing algorithms and techniques to enable U.S. electronic warfare systems to learn to jam new RF threats automatically in the field, instead of waiting for technicians in laboratories to characterize new communications threats and develop countermeasures. This is a clear sign of advancing intelligent technology in electronic warfare.

[Read MilitaryAeroSpace & BLADE]

Home Robotics: Beer Robots

Vikramaditya Jakkula — Mon, 12 Jul 2010 11:05:06 +0000

Willow Garage developed what we can call as a “Beer Robot“: a robot that will not only grab a beer and bring it to you, but will also recognize your face and your preferred beer. The Robot can fetch the beer as well as open it and make it ready for consumption. Let us see the robot in action now:

Educational Data Mining

Vikramaditya Jakkula — Tue, 22 Jun 2010 04:20:03 +0000

Even today Education stands on the list of grand challenges of the 21st century. There are two major areas which cover the breadth(involves reaching broader audience with common goals, and rising literacy level across the globe) and depth(effective and clear learning) of the challenges in education. Carnegie Mellon University played host to the conferences on the use of computers in education. Educational data mining is all about using data gathered from educational services or tools(includes user data or student data), and mine it to find knowledge which when applied would aid the user with better service offering(improved learning or faster efficient learning).

The Third International Conference on Educational Data Mining (EDM2010), which was held June 11-13, and the10th International Conference on Intelligent Tutoring Systems (ITS2010), June 14-18 were held on carnegie mellon campus. The conferences are a clear sign that the educational data mining is emerging into a strong research area.

[Read Dr. Dobbs]

SAP buying Sybase: A $5.8 Billion Deal!

Vikramaditya Jakkula — Fri, 14 May 2010 13:31:43 +0000

SAP buying Sybase for $5.8 billion. The acquisition will allow SAP to own mobile and cloud computing technology which would aid its products future development. The deal will also provide substantial revenue streams and technology stake-hold that it can use to stay competitive with other industry giants.

About SAP:

Founded in 1972, SAP has a rich history of innovation and growth as a true industry leader. SAP currently has sales and development locations in more than 50 countries worldwide and is listed on several exchanges, including the Frankfurt Stock Exchange and NYSE under the symbol “SAP.”

About Sybase:

Sybase has been a leader in developing and expanding innovative database technology. Since our founding in a Berkeley, Calif., home in 1984, we have earned the trust of many of the world’s leading companies for our ability to manage information and deliver unsurpassed levels of data reliability and security. Today, Sybase leads the industry in delivering enterprise software to manage, analyze and mobilize information. We are recognized globally as a performance leader, proven in the most data-intensive industries and across all major systems, networks and devices.

[eWeek]

Can academia projects help businesses? UoA professor says yes!

Vikramaditya Jakkula — Tue, 30 Mar 2010 02:14:44 +0000

Text mining tools can summarize and look for patterns within large electronic documents. Such tools are still expensive and difficult to use on large scale. But a group of researchers, including one’s at the University of Alberta, are hoping to change that.

A University of Alberta professor is helping to create text analysis tools to deeply examine historical trial accounts from the U.K.’s famous Old Bailey criminal court. While the research project is important to academia, the Edmonton-based researcher said that improving the quality of text mining tools could have benefits for businesses as well.

While academia are developing tools like TAPoR, a textual analysis tool that can summarize a body of text, find collocates, identify important dates, and discover the co-occurrences of two target words, the same could be applied to business records as well. Some of the tools in TAPoR use forms of visualization to help researchers grasp the data even clearer.

[IT Canada]

Machine Learning at Stanford University

vinayak — Sun, 28 Mar 2010 11:34:09 +0000

This time we bring to you an online video tutorial on CS 229: Machine Learning course from Stanford University. Besides 19 Lectures on machine learning you will be provided with relevant lecture notes, links other related miscellaneous stuff . Professor Andrew Ng will introduce machine learning and statistical pattern recognition which will cover topics like supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Other applications of machine learning, such as to robotic control, data mining, autonomous navigation, bio informatics, speech recognition, and text and web data processing are also discussed.

This was the first lecture:- (course materials) The first 30 minutes or so of this lecture is introduction to the course and the field of machine learning in general.

In the rest of the lecture, the four main parts of the course is described in some detail along with illustrative examples :-Supervised learning: providing the algorithm a data set, supervising- learn the association between input & output, regression problems, classification problems, support vector machines- infinite number of features
Learning theory
Unsupervised learning: clustering, Cocktail party problem, independent component analysis Reinforcement learning: reward function (good dog, bad dog), feedback function

In the second lecture, Professor Andrew Ng will cover topics like linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

For more visit the home page: http://cs229.stanford.edu/

Annual survey shows the importance & high spirits towards data mining

vinayak — Sun, 28 Mar 2010 11:19:16 +0000

As a service to the data mining community, RexerAnalytics conducts an annual online survey (started in 2009). It analyzes some factors like experiences, priorities, views and challenges being faced by data mining industry. The Third Annual Data Miner Survey results were announced after studying the reports of 710 respondents from the data mining community. It was concluded that data miners and their organization’s are highly confident and happy with their services and analytic capabilities giving a feedback of “above average” or “excellent” performances. Most of them even assured that economy conditions will never be a set back or a weak point for them. According to the survey result, the most commonly used and most satisfying primary data mining tools this year are IBM SPSS Modeler (SPSS Clementine), Statistica, and IBM SPSS Statistics (SPSS Statistics). Open source tool Weka is increasingly used by both academic and for-profit data miners. SAS Enterprise Miner dropped in data miner’s tool rankings this year.

Some highlights:

40-item survey of data miners, conducted on-line in early 2009.

710 participants from 58 countries.

Data miners’ most commonly used algorithms are regression, decision trees,
and cluster analysis.

Half of data miners say their results are helping to drive strategic
decisions and operational processes.

58% say they are adding to the knowledge base in the field.

60% of respondents say the results of their modeling are deployed
always or most of the time.

Most data miners feel that the economy will not negatively impact them.

Almost half of industry data miners rate the analytic capabilities of their
company as above average or excellent. But 19% feel their company has
minimal or no analytic capabilities.

The top challenges facing data miners are dirty data, explaining data mining
to others, and difficult access to data. However, in 2009 fewer data miners
listed data quality and data access as challenges than in the previous year.

IBM SPSS Modeler (SPSS Clementine), Statistica, and IBM SPSS Statistics
(SPSS Statistics) are identified as the “primary tools” used by the most data
miners.

Open-source tools Weka and R made substantial movement up data
miner’s tool rankings this year, and are now used by large numbers of
both academic and for-profit data miners.

SAS Enterprise Miner dropped in data miner’s tool rankings this year.

Users of IBM SPSS Modeler, Statistica, and Rapid Miner are the most
satisfied with their software.

Fields & Industries: Data mining is everywhere. The most sited areas are
CRM / Marketing, Academic, Financial Services, & IT / Telecom. And in the
for-profit sector, the departments data miners most frequently work in are
Marketing & Sales and Research & Development.

[RexterAnalytics]

STRINGS: Recommendation engine with a heart of Data Mining

Vikramaditya Jakkula — Sat, 20 Mar 2010 03:17:02 +0000

A Bellevue, Washington-based company called Strings is trying to do learn online user behavior to recommend products or push targeted advertising.Users can fine-tune Strings to follow different kinds of behavior, whether it’s passive browsing or active behavior “starring”, “liking” or buying items. The company earns revenue through affiliate fees when it successfully recommends products that consumers later buy.

About Strings:

Strings is part of a wave of companies like Blippy and Foursquare that try to incentivize and make users feel comfortable sharing their behavior. Strings is a social tracking and filtering platform that allows you to share and uncover experiences that are relevant to you. Strings incorporates strong privacy controls, easy filtering, and tracking support that allows your actions on and offline to automatically identify personalized trends worth following. Visit http://www.strings.com/about

[Venture Beat]

Data Analysis Platform for Drug Discovery & Development

Vikramaditya Jakkula — Fri, 19 Mar 2010 15:02:56 +0000

Genedata, announced Genedata Analyst, an integrated statistical and data analysis platform with advanced visualization capabilities.Built on client-server architecture, Genedata Analyst handles huge and complex data sets of billion-plus data points while supporting small research groups or hundreds of users. Simultaneously managing data from different sources, Genedata Analyst gives researchers and biostatisticians a secure and scalable data-mining platform, which can be tightly integrated with existing research IT ecosystems.

Features (Selected):

Enhanced Usability enabling non-expert users to access
High Scalability processes large and complex datasets
Advanced Visualization
Tools such as ANOVA, linear models, Principal Components and Partial Least Square analysis, Self-Organizing Maps (SOM) and clustering methods

About Genedata
Genedata transforms data into intelligence with a portfolio of advanced software solutions for drug discovery and life science research, which spans target, lead and biomarker discovery. Used by a majority of the world’s top 50 pharmaceutical companies and leading research organizations, Genedata Phylosopher®, Genedata Screener®, Genedata Expressionist®, and Genedata Analyst™ make research data accessible and understandable, enabling scientific discovery that fights disease and improves health worldwide. Founded in 1997, Genedata is privately held, with headquarters in Basel, Switzerland, and offices in Japan, Germany and the US. www.genedata.com

[GeneData]