[1] SIAM Conference on Data Mining (SDM 2011)
This conference provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. It also provides an ideal setting for graduate students and others new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending tutorials (included with conference registration). A set of focused workshops are also held on the last day of the conference. The proceedings of the conference are published in archival form, and are also made available on the SIAM web site.
Website: http://www.siam.org/meetings/sdm11/

[2] SIGKDD Conference on Knowledge Discovery and Data Mining
The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2011 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.
Website: http://kdd.org/kdd/2011/

[3] ACM SIGMOD 2011
The annual ACM SIGMOD/PODS conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences.
Website: http://www.sigmod2011.org/index.shtml

[4] IJCAI 2011
The IJCAI-11 Program Committee invites submissions of technical papers for IJCAI-11, to be held in Barcelona, Spain, July 19-22, 2011. Submissions are invited on significant, original, and previously unpublished research on all aspects of artificial intelligence. The theme of IJCAI-11 is “Integrated and Embedded Artificial Intelligence” (IEAI) with a focus on artificial intelligence that crosses discipline boundaries within AI, and between AI and other disciplines. Building systems often requires techniques from more than one area (e.g. both machine learning and natural language processing, or both planning and preference representation). In addition, larger systems often have AI components embedded within that provide intelligent functionalities such as learning and reasoning. The conference will include a special track dedicated to such work.
Website: http://ijcai-11.iiia.csic.es/

[5] ECML PKDD 2011
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) will take place in Athens, Greece from September 5th to 9th, 2011. This event builds upon a very successful series of 21 ECML and 14 PKDD conferences, which have been jointly organized for the past ten years.
Website: http://www.ecmlpkdd2011.org/

Other Conferences:

[6] TDWI World Series Conference
What is your corporate data strategy? Most business and IT professionals point to their data models, data quality tools, or even a spreadsheet data “dictionary.” Maybe you’re just getting started with a new data quality project. Maybe you already have several data management initiatives in place and are making good progress. Or perhaps you’ve recently inherited the mess someone else made. In any case, does your right hand know what your left is up to? Several departmental data initiatives—even successful ones—do not equal a corporate data strategy. TDWI fosters a community of learning where business and technical professionals come together to gain knowledge and skills, network with peers, and advance their careers. Through education and research programs, TDWI enables individuals, teams, and organizations to leverage information to improve decision making, optimize performance, and achieve business objectives.
Website: http://events.tdwi.org/events/las-vegas-world-conference-2011/home.aspx?utm_source=AttendeeMktg&utm_medium=E-Mail&utm_campaign=lv20g

Realizing the popularity, and the need of Data Mining in the road ahead, has resulted into organizing many more conferences (also conferences into  many more specialized data mining streams) to be held all over the world. So, many more conferences, lots of research and great innovations and discoveries lay ahead.

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.

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

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]

Today’s industry generates large volumes of data from all sectors such as  financial, retail,  factory, call centers, and customer products,  and so forth, SAS Analytics lets them realize the value within these growing volumes of data.

[Read more @ SAS]

This is a clear indication of what we can expect in the near future.(Oh boy! Not another American Robot idol, The Amazing robot race, America’s next top robot model, or robot factor)

Read more about the event here and for sure visit the compiled photo gallery1 and gallery2 for more pictures.

The robot hand is capable of 24 movements and can be remote-operated with the CyberGlove Photograph: Kim Kyung-hoon/Reuters

Humanoid industrial robot 'Motoman-SDA5D', developed by the Yaskawa Electric Corporation, demonstrates its capabilities with Lego Photograph: Dai Kurokawa/EPA

A humanoid robot 'Manoi AT01', produced by Japan's toy robot maker Kyosho, performs a hip-hop dance Photograph: Yoshikazu Tsuno/AFP/Getty Images

A description:

Microsoft:

Amazon:

Yahoo!:

Google:

IBM:

Sources:

• Wikipedia,
• Yahoo! Media Relations,
• Microsoft PressPass,
• Google Press Center,
• Google Timeline
• PowerPivotPro.com
• Microsoft Research
• Amazon Media Room
• InformationWeek
• NYTimes

–  SAGAR JAUHARI, SDE Intern.

Graduate student Quan Sun’s win at the University of California San Diego student datamining competition surely highlights Weka. Sun claims he used open source software to claim his win in the elite “hard” category for graduate students. In fact, says Sun, at least half the competitors in the competition used the software, called Weka, which he describes as the “Microsoft Word of data mining”.

Waikato Environment for Knowledge Analysis (WEKA), developed at the University of Waikato, New Zealand, is a collection of machine learning algorithms. It has data preprocessing tools to provide inputs to these algoritms.This tool uses Java as its base and is compatible with both Windows and Linux. It is open source software available under the terms of GNU General Public License. Inputs to the machine learning algorithms implemented in WEKA are in the form of relational tables in Attribute Relational File Format (ARFF).

Some of the key features of WEKA

• WEKA has inbuilt format converters to convert the dataset available in any format say a spreadsheet to ARFF type. In addition it also incorporates filter to delete specified attributes from the dataset.
• WEKA trains and tests the learning algorithms that perform classification and regression and also allows the user to create his own classifier interactively.
• WEKA allows the user to handle cluster and their instances.Inaddition it provides access to several methods for attribute selection that might involve either the full data set or a cross validation.

Working with WEKA is very simple. This is mainly because it uses a GUI Explorer. The classification process is not cumbersome, as it involves the selection of the attributes to be related and the algorithm to be used, by the user. The results provide a matrix of both the classified and the misclassified data. The classification error mentioning the mean and the standard deviation are also displayed. WEKA helps in realizing the goal of data mining; by predicting missing values and validating that the predicted values are correct. WEKA is a tool that permits users to develop and analyze new machine learning algorithms to make their job easier.

What can weka do?

Preprocess – Weka has file format converters for spreadsheets, C4.5 file formats and serialized instances. It can also open a URL and use HTTP to download an ARFF file from the Web or open a database using JDBC, and retrieve instances using SQL. It also provides a list of filters to delete specified attributes from a dataset.

Classify – Weka trains and tests learning schemes that perform classification or regression. The classifiers can be divided into Bayesian, trees, rules, functions and lazy. It also builds a linear regression model and allows the user to build their own classifiers interactively. It also provides options for a number of meta learners.

Cluster – Weka shows the clusters and the number of instances in the cluster. Thereafter it determines the majority class in each cluster and gives the confusion matrix.

Associate – Weka contains three algorithms for determining association rules-apriory, predictive apriory and filtered associators.

Attribute Selection – Weka gives access to several methods for attribute selection, which involves an attribute evaluator and a search method. Attribute selection can be performed using the full training set or cross-validation.

Visualization – It displays a matrix of two-dimensional scatter plots of each pair of attributes.

Other ML/DM software (R, Weka, Yale)

Lluís Belanche

Processing huge datasets

If the dataset is too huge, running to a few thousand attributes and a few lakh records, can lead Weka into ‘OutOfMemory’ exception. Most Java virtual machines allocate a certain maximum amount of memory which is much less than the amount of RAM to run Java programs. However, we can extend the memory available for the virtual machine by setting appropriate options. For large data processing we can take a look at Mahout, an open source scalable, Apache licensed machine learning libraries

Read more at computer world.

The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-09 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.

The registration is now open. The program is made available. Students make sure to visit travel grants section to apply for awards. And for all those who plan to travel check out the deals posted.

For those intrested to see some interesting talks from KDD 2008, videos are available.

Venue: 17 Boulevard Saint-Jacques, 75014 Paris, France