Fwd: [hyd-swe-all] Fwd: Tech Talk: Co-occurrence Analysis: A Data Mining framework for finding interesting needles in crazy haystacks! By Dr. Shailesh Kumar, Google India.

[Sekhar Muddana]

---------- Forwarded message ----------
From: Vikram Manney <vikr...@google.com>
Date: Tue, Jun 4, 2013 at 12:26 PM
Subject: Tech Talk: Co-occurrence Analysis: A Data Mining framework for finding interesting needles in crazy haystacks! By Dr. Shailesh Kumar, Google India.

Hi All,

Co-occurrence Analysis: A Data Mining framework for finding interesting needles in crazy haystacks! By Dr. Shailesh Kumar, Google India.

Most data around us can be thought of as "things co-occurring with other things in certain contexts". Whether it is products co-occurring with other products in retail market baskets, words occurring before or after other words in unstructured text, tags co-occurring with other tags in social tagging systems, people co-occurring with other people in various social networking scenarios, or objects occurring in various 2-D geometrical juxtapositions of other objects in images, etc.

While there have been silos of efforts in each research community - retail, text, social networking, and vision, etc. - in dealing with "their" data, there has been no unifying framework to tame such a wide variety of co-occurrence data systematically - a theme for this talk.

We will present a simple, intuitive, yet a powerful co-occurrence analytics framework to deal with a wide variety of data of the form "things co-occurring with other things in some context". After describing the framework we will demonstrate how to adapt and apply the core principles of the framework to a variety of large real-world datasets to find novel and actionable insights even in the presence of significant noise in the data. 

Specifically, we will describe how to find (a) Product bundles in Retail Point of Sales data, (b) Communities in Tag Networks and (c) Meaningful Phrases in Text Data using this framework. 

What makes this approach attractive is that it is:

Unsupervised: No cost of getting labeled data. Just point it to the data and crunch.

Unbiased: No prior assumptions about data distributions, etc.

High Precision: Generates very high quality insights.

High Recall: Generates exhaustively many insights.

Parameter Poor: Very few parameters to play with.

Scaleable: Highly parallelizable in MapReduce sense.

Universal: Can be applied to a wide variety of domains and applications.

Details of the event:

Date: 20th June 2013, Thursday

Registration starts: 5 pm

Talk Timings: 5:30 PM - 6:30 PM

High Tea: 6:30 PM onwards

Venue:

Hyderabad:
Google India
Divyasree Omega,
Plot No: 13/E, Kondapur,
Hitech city, Hyderabad. 
*Adj. to ICICI Bank.

We recommend you register for the event in advance, limited seats available. You can access the registration form here. 

Please feel free to contact on googleindi...@google.com if you have any queries regarding registration or the talk. We will revert back with your registration confirmation a couple of days before the talk. 

Hoping to see you all there !!!!
Google India Tech Talk team

 

[Nageshbhattu Sristi]

Dear Sir,

  I am a phd student in NIT Warangal working in the area of text mining. I wanted to attend this talk but could not get any confirmation yet. I am working under the guidance of Prof.D.V.L.Somayajulu and working on Posterior Regularization applications. As part of my research I have studied spectral clustering and semi-supervised approaches and other things. I thought the title of the talk is very much with in my research area and wanted to attend the same. I would be thankful if you could give me any information in this regard. I have attended previously held talks convened by HydACM (both @microsoft) and looking forward for a positive reply.

Thanks,

Nagesh Bhattu,

Research Scholar,

NIT Warangal. 

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