The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

Theme and format

The Fifth Elephant 2017 is a four-track conference on:

  1. Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
  2. Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
  3. Hands-on tutorials on data mining tools, and ML platforms and techniques.
  4. Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.

The Fifth Elephant is a conference for practitioners, by practitioners.

Talk submissions are now closed.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

About the conference

This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.

We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:

  • Full-length, 40-minute talks.
  • Crisp, 15-minute talks.
  • Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
  • Off-the-record (OTR) sessions of 60-90 minutes duration.

Selection Process

  1. Proposals will be filtered and shortlisted by an Editorial Panel.
  2. Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
  3. Proposers are also encouraged to vote and comment on other proposals submitted here.

Selection Process Flowchart

We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.

Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers.

Travel grants

Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.

Commitment to Open Source

We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.

Important Dates:

  • Deadline for submitting proposals: June 10
  • First draft of the coference schedule: June 20
  • Tutorial and workshop announcements: June 20
  • Final conference schedule: July 5
  • Conference dates: 27-28 July

Contact

For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.


Related events

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Simrat Hanspal

@simrathanspal

Interestingness of interestingness measures

Submitted Apr 30, 2017

Analysis of relationship between entities is at the heart of data mining problems. There are many metrics used for association mining like support, confidence, lift, mutual information etc. However many of these measures provide conflicting results about the interestingness of the association. Therefore it becomes very important to understand how to evaluate metrics for an application.

Outline

A fundamental step in pattern mining of transactional datasets is the extraction of frequent and interesting itemsets - a set of entities connected by the frequently occurring relationships between them. For instance, identifying from housing purchase data the correlations to age and income groups they belong to can lead to explainable relationships between the different data points which in turn leads to knowledge discovery. This kind of analysis is often confounded by the presence of spurious correlations and data sparsity especially in e-commerce where much of the traffic is often directed to a small percentage of the catalog.

Data mining is the process of discovering previously unknown interesting relationships which can be used to increase customer engagement and boost the sales. One very common and interesting example of this would be the discovery of strong link between diapers and beers from transactional data of walmart. This association was definitely not intuitive because you would expect customers to buy other baby products along with diapers. It was then suspected that these purchases were made by fathers baby sitting on weekends. Even Though this was found to be a strong correlation we must not forget that correlation doesn’t imply causation. A strong correlation is an interesting insight from user behaviour which can be fed back to increase customer interaction.

The goal of this talk is to set the stage for mining of associations, present commonly used techniques, followed by objective measures of interestingness of associations and finally, present an analysis on real customer datasets. In the case of sites attracting large amounts of customer traffic the main challenge is to make this process computationally effective.
We will pay special attention to low-traffic situations so that we can mine for interesting patterns without requiring large amount of data to support it. One way in which we achieve this is to build higher-level abstractions for entities such as the brand of the product rather than the product itself. In addition to this domain knowledge and transitive connections can be used to help with the cold-start problem.

Brief outline of the talk -

** What is association mining **

** Motivation - Market basket analysis **

** Association mining: Basic concepts **

  • Frequent ItemSet Generation

  • Association Generation

    • Apriori algorithm
    • FP growth algorithm

** Evaluation of association patterns**

  • Measures of interestingness

  • Case studies to compare interestingness measures

** Challenges of mining sparse dataset **

** Unearthing unseen gems**

  • Motivation for mining transitive or indirect association

  • Popular approaches

** Key takeaways **

Speaker bio

Simrat is a Data Scientist, Engineering Ninja and Inspector Gadget at Mad Street Den. She builds data platforms and models to make sense of user and product data in e-commerce online retail.

Links

Slides

https://drive.google.com/file/d/0ByY7dK7M7e-dc2gtdG15RlVJVU0/view?usp=sharing

Comments

{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more