Trjtdtk

Am I ethically obligated to go into work on an off day if the reason is sudden?

What information about me do stores get via my credit card?

"... to apply for a visa" or "... and applied for a visa"?

system call string length limit

What aspect of planet Earth must be changed to prevent the industrial revolution?

University's motivation for having tenure-track positions

How do you keep chess fun when your opponent constantly beats you?

How did the audience guess the pentatonic scale in Bobby McFerrin's presentation?

How many people can fit inside Mordenkainen's Magnificent Mansion?

Are spiders unable to hurt humans, especially very small spiders?

What was the last x86 CPU that did not have the x87 floating-point unit built in?

Can a 1st-level character have an ability score above 18?

Why can't wing-mounted spoilers be used to steepen approaches?

Can the DM override racial traits?

A pet rabbit called Belle

How to delete random line from file using Unix command?

What's the point in a preamp?

He got a vote 80% that of Emmanuel Macron’s

How to pronounce 1ターン?

Arduino Pro Micro - switch off LEDs

How did passengers keep warm on sail ships?

What can I do if neighbor is blocking my solar panels intentionally?

Converting from Markdown-with-biblatex-commands to LaTeX

Did the UK government pay "millions and millions of dollars" to try to snag Julian Assange?

Clustering of multi-label data

0

$begingroup$

The dataset consists of

1) a set of objects and

2) a set of labels, which are used to describe the objects.

For the moment, for simplicity sake, each label can be marked as either true or false (In a more complex setup, each label will have a value of 1-10).

But, not all the labels are actually applied to all the objects (in principle, all the labels can and should be applied across all the objects, but in practice, they just are not). Also, when a label isn't applied to an object, one cannot simply assume that the label's value for that particular is false. Therefore, the missing labels will be ignored in the model.

I need to cluster the objects based on their labels.

Any tips on how and what algorithms to use will be appreciated.

classification clustering multilabel-classification labels

share|improve this question

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  First you need to decide whether you want to do clustering (ignore the labels?) or classification (predict missing labels).
  $endgroup$
  – Anony-Mousse
  Mar 31 at 7:13
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Ignore the missing labels. Wrongly predicted missing labels can mess things up.
  $endgroup$
  – Yogesch
  Mar 31 at 7:42
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  That sounds pretty much like the standard setup of recommender systems?
  $endgroup$
  – Anony-Mousse
  Mar 31 at 10:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Ok, maybe... At first look, the crux to any sort of clustering in a recommendation system is to be able to define a "distance" metric between arbitrary points (objects). For each point/object, I have a set L1, L2, ... Ln where Ln can be 0 or 1, or na. So now how do I invent this "distance" metric in a consistent/coherent way? Should that be another question? Sorry, I'm yet to figure out what's a trivial question and what's a serious question in the datascience business.
  $endgroup$
  – Yogesch
  Mar 31 at 15:57
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Consider each label to be a user!
  $endgroup$
  – Anony-Mousse
  Apr 1 at 5:42
  

  
  
  
  

 | 
show 5 more comments

0

$begingroup$

The dataset consists of

1) a set of objects and

2) a set of labels, which are used to describe the objects.

For the moment, for simplicity sake, each label can be marked as either true or false (In a more complex setup, each label will have a value of 1-10).

But, not all the labels are actually applied to all the objects (in principle, all the labels can and should be applied across all the objects, but in practice, they just are not). Also, when a label isn't applied to an object, one cannot simply assume that the label's value for that particular is false. Therefore, the missing labels will be ignored in the model.

I need to cluster the objects based on their labels.

Any tips on how and what algorithms to use will be appreciated.

classification clustering multilabel-classification labels

share|improve this question

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  First you need to decide whether you want to do clustering (ignore the labels?) or classification (predict missing labels).
  $endgroup$
  – Anony-Mousse
  Mar 31 at 7:13
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Ignore the missing labels. Wrongly predicted missing labels can mess things up.
  $endgroup$
  – Yogesch
  Mar 31 at 7:42
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  That sounds pretty much like the standard setup of recommender systems?
  $endgroup$
  – Anony-Mousse
  Mar 31 at 10:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Ok, maybe... At first look, the crux to any sort of clustering in a recommendation system is to be able to define a "distance" metric between arbitrary points (objects). For each point/object, I have a set L1, L2, ... Ln where Ln can be 0 or 1, or na. So now how do I invent this "distance" metric in a consistent/coherent way? Should that be another question? Sorry, I'm yet to figure out what's a trivial question and what's a serious question in the datascience business.
  $endgroup$
  – Yogesch
  Mar 31 at 15:57
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Consider each label to be a user!
  $endgroup$
  – Anony-Mousse
  Apr 1 at 5:42
  

  
  
  
  

 | 
show 5 more comments

$begingroup$

The dataset consists of

1) a set of objects and

2) a set of labels, which are used to describe the objects.

For the moment, for simplicity sake, each label can be marked as either true or false (In a more complex setup, each label will have a value of 1-10).

But, not all the labels are actually applied to all the objects (in principle, all the labels can and should be applied across all the objects, but in practice, they just are not). Also, when a label isn't applied to an object, one cannot simply assume that the label's value for that particular is false. Therefore, the missing labels will be ignored in the model.

I need to cluster the objects based on their labels.

Any tips on how and what algorithms to use will be appreciated.

classification clustering multilabel-classification labels

share|improve this question

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

$endgroup$

share|improve this question

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

share|improve this question

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

edited Mar 31 at 8:41

Damini Jain

1136

edited Mar 31 at 8:41

Damini Jain

1136

asked Mar 31 at 5:54

YogeschYogesch

1013

asked Mar 31 at 5:54

YogeschYogesch

1013