Hi,

Is there any implementation for Sequential Pattern Mining in Mahout? I
see there is an implementation of Sequential Pattern Mining but I am
unsure if it can be used for my use case.

Thanks,
Nishant

Can you tell something about your use case?

Paritosh

On 27-11-2011 15:14, Nishant Chandra wrote:
> Hi,
>
> Is there any implementation for Sequential Pattern Mining in Mahout? I
> see there is an implementation of Sequential Pattern Mining but I am
> unsure if it can be used for my use case.
>
> Thanks,
> Nishant
>
>
> -----
> No virus found in this message.
> Checked by AVG - www.avg.com
> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11

Use case is related to purchase transactions.

Sample data set:
Customer ID Acquisition time Products
101 30 June 2007 Product 1
101 12 August 2007 Product 3
101 20 December 2008 Product 4
102 10 September 2008 Product 3
102 12 September 2008 Product 5
102 20 January 2009 Product 5.....

Sample rule:
Rule ID Consequent Antecedents                        Support %   Confidence %
Rule 1   Product 4    Product 1 then Product 3        57.1                75.0

I want to identify rules such as: after acquiring product 1 and then
product 3, customers have an increased likelihood
(75%) of purchasing product 4 next.

Thanks,
Nishant
On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan <[EMAIL PROTECTED]> wrote:
> Can you tell something about your use case?
>
> Paritosh
>
> On 27-11-2011 15:14, Nishant Chandra wrote:
>>
>> Hi,
>>
>> Is there any implementation for Sequential Pattern Mining in Mahout? I
>> see there is an implementation of Sequential Pattern Mining but I am
>> unsure if it can be used for my use case.
>>
>> Thanks,
>> Nishant
>>
>>
>> -----
>> No virus found in this message.
>> Checked by AVG - www.avg.com
>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>
>

Have you checked out the recommendation algorithms? I think this can be
easily done using them.

Paritosh

On 27-11-2011 15:39, Nishant Chandra wrote:
> Use case is related to purchase transactions.
>
> Sample data set:
> Customer ID Acquisition time Products
> 101 30 June 2007 Product 1
> 101 12 August 2007 Product 3
> 101 20 December 2008 Product 4
> 102 10 September 2008 Product 3
> 102 12 September 2008 Product 5
> 102 20 January 2009 Product 5.....
>
> Sample rule:
> Rule ID Consequent Antecedents                        Support %   Confidence %
> Rule 1   Product 4    Product 1 then Product 3        57.1                75.0
>
> I want to identify rules such as: after acquiring product 1 and then
> product 3, customers have an increased likelihood
> (75%) of purchasing product 4 next.
>
> Thanks,
> Nishant
>
>
> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan<[EMAIL PROTECTED]>  wrote:
>> Can you tell something about your use case?
>>
>> Paritosh
>>
>> On 27-11-2011 15:14, Nishant Chandra wrote:
>>> Hi,
>>>
>>> Is there any implementation for Sequential Pattern Mining in Mahout? I
>>> see there is an implementation of Sequential Pattern Mining but I am
>>> unsure if it can be used for my use case.
>>>
>>> Thanks,
>>> Nishant
>>>
>>>
>>> -----
>>> No virus found in this message.
>>> Checked by AVG - www.avg.com
>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>
>
> -----
> No virus found in this message.
> Checked by AVG - www.avg.com
> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11

Are you talking about CF? Can you please explain a bit?

To be clear, for my use case, temporal sequence is important.

Nishant

On Sun, Nov 27, 2011 at 3:44 PM, Paritosh Ranjan <[EMAIL PROTECTED]> wrote:
> Have you checked out the recommendation algorithms? I think this can be
> easily done using them.
>
> Paritosh
>
> On 27-11-2011 15:39, Nishant Chandra wrote:
>>
>> Use case is related to purchase transactions.
>>
>> Sample data set:
>> Customer ID Acquisition time Products
>> 101 30 June 2007 Product 1
>> 101 12 August 2007 Product 3
>> 101 20 December 2008 Product 4
>> 102 10 September 2008 Product 3
>> 102 12 September 2008 Product 5
>> 102 20 January 2009 Product 5.....
>>
>> Sample rule:
>> Rule ID Consequent Antecedents                        Support %
>> Confidence %
>> Rule 1   Product 4    Product 1 then Product 3        57.1
>>  75.0
>>
>> I want to identify rules such as: after acquiring product 1 and then
>> product 3, customers have an increased likelihood
>> (75%) of purchasing product 4 next.
>>
>> Thanks,
>> Nishant
>>
>>
>> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan<[EMAIL PROTECTED]>
>>  wrote:
>>>
>>> Can you tell something about your use case?
>>>
>>> Paritosh
>>>
>>> On 27-11-2011 15:14, Nishant Chandra wrote:
>>>>
>>>> Hi,
>>>>
>>>> Is there any implementation for Sequential Pattern Mining in Mahout? I
>>>> see there is an implementation of Sequential Pattern Mining but I am
>>>> unsure if it can be used for my use case.
>>>>
>>>> Thanks,
>>>> Nishant
>>>>
>>>>
>>>> -----
>>>> No virus found in this message.
>>>> Checked by AVG - www.avg.com
>>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>>
>>
>> -----
>> No virus found in this message.
>> Checked by AVG - www.avg.com
>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>
>

I am talking about the implementations available in Mahout where you can
find similarity between users by analyzing some datamodel and then
recommend items based on that.

If this can solve your problem. I see this implemented in Mahout. And
its very easy to use.

On 27-11-2011 15:47, Nishant Chandra wrote:
> Are you talking about CF? Can you please explain a bit?
>
> To be clear, for my use case, temporal sequence is important.
>
> Nishant
>
> On Sun, Nov 27, 2011 at 3:44 PM, Paritosh Ranjan<[EMAIL PROTECTED]>  wrote:
>> Have you checked out the recommendation algorithms? I think this can be
>> easily done using them.
>>
>> Paritosh
>>
>> On 27-11-2011 15:39, Nishant Chandra wrote:
>>> Use case is related to purchase transactions.
>>>
>>> Sample data set:
>>> Customer ID Acquisition time Products
>>> 101 30 June 2007 Product 1
>>> 101 12 August 2007 Product 3
>>> 101 20 December 2008 Product 4
>>> 102 10 September 2008 Product 3
>>> 102 12 September 2008 Product 5
>>> 102 20 January 2009 Product 5.....
>>>
>>> Sample rule:
>>> Rule ID Consequent Antecedents                        Support %
>>> Confidence %
>>> Rule 1   Product 4    Product 1 then Product 3        57.1
>>>   75.0
>>>
>>> I want to identify rules such as: after acquiring product 1 and then
>>> product 3, customers have an increased likelihood
>>> (75%) of purchasing product 4 next.
>>>
>>> Thanks,
>>> Nishant
>>>
>>>
>>> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan<[EMAIL PROTECTED]>
>>>   wrote:
>>>> Can you tell something about your use case?
>>>>
>>>> Paritosh
>>>>
>>>> On 27-11-2011 15:14, Nishant Chandra wrote:
>>>>> Hi,
>>>>>
>>>>> Is there any implementation for Sequential Pattern Mining in Mahout? I
>>>>> see there is an implementation of Sequential Pattern Mining but I am
>>>>> unsure if it can be used for my use case.
>>>>>
>>>>> Thanks,
>>>>> Nishant
>>>>>
>>>>>
>>>>> -----
>>>>> No virus found in this message.
>>>>> Checked by AVG - www.avg.com
>>>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>> -----
>>> No virus found in this message.
>>> Checked by AVG - www.avg.com
>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>
>
> -----
> No virus found in this message.
> Checked by AVG - www.avg.com
> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>

I don't want to go off topic but perhaps its possible to use cf by
creating super product id's of combined purchases
101 1
101 2
> implies 101 3 where 3 is a product which represents the purchase of 1 and 2 in a time frame. users / items associated with 3 have a pref for item / users ...

However I know its not your original focus of the your question so
maybe theiris a much better way

lee c

On 27 November 2011 10:17, Nishant Chandra <[EMAIL PROTECTED]> wrote:
> Are you talking about CF? Can you please explain a bit?
>
> To be clear, for my use case, temporal sequence is important.
>
> Nishant
>
> On Sun, Nov 27, 2011 at 3:44 PM, Paritosh Ranjan <[EMAIL PROTECTED]> wrote:
>> Have you checked out the recommendation algorithms? I think this can be
>> easily done using them.
>>
>> Paritosh
>>
>> On 27-11-2011 15:39, Nishant Chandra wrote:
>>>
>>> Use case is related to purchase transactions.
>>>
>>> Sample data set:
>>> Customer ID Acquisition time Products
>>> 101 30 June 2007 Product 1
>>> 101 12 August 2007 Product 3
>>> 101 20 December 2008 Product 4
>>> 102 10 September 2008 Product 3
>>> 102 12 September 2008 Product 5
>>> 102 20 January 2009 Product 5.....
>>>
>>> Sample rule:
>>> Rule ID Consequent Antecedents                        Support %
>>> Confidence %
>>> Rule 1   Product 4    Product 1 then Product 3        57.1
>>>  75.0
>>>
>>> I want to identify rules such as: after acquiring product 1 and then
>>> product 3, customers have an increased likelihood
>>> (75%) of purchasing product 4 next.
>>>
>>> Thanks,
>>> Nishant
>>>
>>>
>>> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan<[EMAIL PROTECTED]>
>>>  wrote:
>>>>
>>>> Can you tell something about your use case?
>>>>
>>>> Paritosh
>>>>
>>>> On 27-11-2011 15:14, Nishant Chandra wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> Is there any implementation for Sequential Pattern Mining in Mahout? I
>>>>> see there is an implementation of Sequential Pattern Mining but I am
>>>>> unsure if it can be used for my use case.
>>>>>
>>>>> Thanks,
>>>>> Nishant
>>>>>
>>>>>
>>>>> -----
>>>>> No virus found in this message.
>>>>> Checked by AVG - www.avg.com
>>>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>>>
>>>
>>> -----
>>> No virus found in this message.
>>> Checked by AVG - www.avg.com
>>> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>>
>>
>

On 27.11.2011 Nishant Chandra wrote:
> I want to identify rules such as: after acquiring product 1 and then
> product 3, customers have an increased likelihood
> (75%) of purchasing product 4 next.

What is your goal with discovering these rules? Assuming what you want is
implementing a feature that recommends items to customers they are likely to
buy:

Did you check the fpgrowth implementation already? Though it does not cover the
temporal aspect you mention it might still be of value for you as it is capable
of discovering items that are typically puchased together.

If you would rather personalize your offerings to the preferences of each of
your customers you might be better of taking a closer look at the collaborative
filtering implementations of Mahout.
Isabel

There are several good ways to deal with this.  The idea of super-products
which are composite features that are derived from history is a good one.
 I would recommend that you limit the number of such super features by
first finding which products cooccur within a reasonable time window more
than you would expect.

The cooccurrence analysis system in Mahout can be misused for this analysis
by building one document per user per sliding window period.  This is a bit
flawed since the sliding windows overlap and thus the appearances of a
transaction in multiple documents is not really an indication of
independent appearances.  Also, the intermediate window documents are much
larger than you might like and they won't take ordering into account.

A better approach is to adapt the current code.  The basic data you need to
collect are:

- the number of times each product appears in a single users transaction
history before another product.

- the number of times each product appears in a transaction history after
another product

- the number of times product i appears after product j.

You can then use the LLR code in Mahout to find cases where a product
sequence occurs anomalously often.  You can then use a Bloom filter or
similar data structure to analyze histories so that you emit product and
super-products as input to a conventional collaborative filtering analysis.
The second major approach to this problem is to build a separate classifier
for each product of interest.  I wouldn't recommend that if you have lots
of possible products, but this can work very well if you have a reasonably
small number of products (say a few hundred or thousand) that you might be
about to recommend.
On Sun, Nov 27, 2011 at 2:09 AM, Nishant Chandra
<[EMAIL PROTECTED]>wrote:

> Use case is related to purchase transactions.
>
> Sample data set:
> Customer ID Acquisition time Products
> 101 30 June 2007 Product 1
> 101 12 August 2007 Product 3
> 101 20 December 2008 Product 4
> 102 10 September 2008 Product 3
> 102 12 September 2008 Product 5
> 102 20 January 2009 Product 5.....
>
> Sample rule:
> Rule ID Consequent Antecedents                        Support %
> Confidence %
> Rule 1   Product 4    Product 1 then Product 3        57.1
>  75.0
>
> I want to identify rules such as: after acquiring product 1 and then
> product 3, customers have an increased likelihood
> (75%) of purchasing product 4 next.
>
> Thanks,
> Nishant
>
>
> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan <[EMAIL PROTECTED]>
> wrote:
> > Can you tell something about your use case?
> >
> > Paritosh
> >
> > On 27-11-2011 15:14, Nishant Chandra wrote:
> >>
> >> Hi,
> >>
> >> Is there any implementation for Sequential Pattern Mining in Mahout? I
> >> see there is an implementation of Sequential Pattern Mining but I am
> >> unsure if it can be used for my use case.
> >>
> >> Thanks,
> >> Nishant
> >>
> >>
> >> -----
> >> No virus found in this message.
> >> Checked by AVG - www.avg.com
> >> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
> >
> >
>

Hi Ted,

I dont understand the composite features and super-products that you
mentioned. Please explain a bit. Are you pointing to a specific data
mining method?

Thanks,
Nishant

On Mon, Nov 28, 2011 at 5:44 AM, Ted Dunning <[EMAIL PROTECTED]> wrote:
> There are several good ways to deal with this.  The idea of super-products
> which are composite features that are derived from history is a good one.
>  I would recommend that you limit the number of such super features by
> first finding which products cooccur within a reasonable time window more
> than you would expect.
>
> The cooccurrence analysis system in Mahout can be misused for this analysis
> by building one document per user per sliding window period.  This is a bit
> flawed since the sliding windows overlap and thus the appearances of a
> transaction in multiple documents is not really an indication of
> independent appearances.  Also, the intermediate window documents are much
> larger than you might like and they won't take ordering into account.
>
> A better approach is to adapt the current code.  The basic data you need to
> collect are:
>
> - the number of times each product appears in a single users transaction
> history before another product.
>
> - the number of times each product appears in a transaction history after
> another product
>
> - the number of times product i appears after product j.
>
> You can then use the LLR code in Mahout to find cases where a product
> sequence occurs anomalously often.  You can then use a Bloom filter or
> similar data structure to analyze histories so that you emit product and
> super-products as input to a conventional collaborative filtering analysis.
>
>
> The second major approach to this problem is to build a separate classifier
> for each product of interest.  I wouldn't recommend that if you have lots
> of possible products, but this can work very well if you have a reasonably
> small number of products (say a few hundred or thousand) that you might be
> about to recommend.
>
>
> On Sun, Nov 27, 2011 at 2:09 AM, Nishant Chandra
> <[EMAIL PROTECTED]>wrote:
>
>> Use case is related to purchase transactions.
>>
>> Sample data set:
>> Customer ID Acquisition time Products
>> 101 30 June 2007 Product 1
>> 101 12 August 2007 Product 3
>> 101 20 December 2008 Product 4
>> 102 10 September 2008 Product 3
>> 102 12 September 2008 Product 5
>> 102 20 January 2009 Product 5.....
>>
>> Sample rule:
>> Rule ID Consequent Antecedents                        Support %
>> Confidence %
>> Rule 1   Product 4    Product 1 then Product 3        57.1
>>  75.0
>>
>> I want to identify rules such as: after acquiring product 1 and then
>> product 3, customers have an increased likelihood
>> (75%) of purchasing product 4 next.
>>
>> Thanks,
>> Nishant
>>
>>
>> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan <[EMAIL PROTECTED]>
>> wrote:
>> > Can you tell something about your use case?
>> >
>> > Paritosh
>> >
>> > On 27-11-2011 15:14, Nishant Chandra wrote:
>> >>
>> >> Hi,
>> >>
>> >> Is there any implementation for Sequential Pattern Mining in Mahout? I
>> >> see there is an implementation of Sequential Pattern Mining but I am
>> >> unsure if it can be used for my use case.
>> >>
>> >> Thanks,
>> >> Nishant
>> >>
>> >>
>> >> -----
>> >> No virus found in this message.
>> >> Checked by AVG - www.avg.com
>> >> Version: 10.0.1411 / Virus Database: 2092/4041 - Release Date: 11/26/11
>> >
>> >
>>
>

OK.

Suppose that people who buy milk and chocolate chip cookies are good
prospects for buying life insurance, but buy either product alone is not a
strong indicator.  You can build a special feature
milk_and_chocolate_chip_cookies in addition to the separate features for
the individual milk and chocolate_chip_cookies products.  The composite
features can be order specific or not.

The potential number of such features is huge.  Clearly, a first cut is to
limit your consideration to composites that actually appear.  You probably
should limit the number that you consider even more stringently than that.

On Mon, Nov 28, 2011 at 5:21 AM, Nishant Chandra
<[EMAIL PROTECTED]>wrote:

> Hi Ted,
>
> I dont understand the composite features and super-products that you
> mentioned. Please explain a bit. Are you pointing to a specific data
> mining method?
>
> Thanks,
> Nishant
>
> On Mon, Nov 28, 2011 at 5:44 AM, Ted Dunning <[EMAIL PROTECTED]>
> wrote:
> > There are several good ways to deal with this.  The idea of
> super-products
> > which are composite features that are derived from history is a good one.
> >  I would recommend that you limit the number of such super features by
> > first finding which products cooccur within a reasonable time window more
> > than you would expect.
> >
> > The cooccurrence analysis system in Mahout can be misused for this
> analysis
> > by building one document per user per sliding window period.  This is a
> bit
> > flawed since the sliding windows overlap and thus the appearances of a
> > transaction in multiple documents is not really an indication of
> > independent appearances.  Also, the intermediate window documents are
> much
> > larger than you might like and they won't take ordering into account.
> >
> > A better approach is to adapt the current code.  The basic data you need
> to
> > collect are:
> >
> > - the number of times each product appears in a single users transaction
> > history before another product.
> >
> > - the number of times each product appears in a transaction history after
> > another product
> >
> > - the number of times product i appears after product j.
> >
> > You can then use the LLR code in Mahout to find cases where a product
> > sequence occurs anomalously often.  You can then use a Bloom filter or
> > similar data structure to analyze histories so that you emit product and
> > super-products as input to a conventional collaborative filtering
> analysis.
> >
> >
> > The second major approach to this problem is to build a separate
> classifier
> > for each product of interest.  I wouldn't recommend that if you have lots
> > of possible products, but this can work very well if you have a
> reasonably
> > small number of products (say a few hundred or thousand) that you might
> be
> > about to recommend.
> >
> >
> > On Sun, Nov 27, 2011 at 2:09 AM, Nishant Chandra
> > <[EMAIL PROTECTED]>wrote:
> >
> >> Use case is related to purchase transactions.
> >>
> >> Sample data set:
> >> Customer ID Acquisition time Products
> >> 101 30 June 2007 Product 1
> >> 101 12 August 2007 Product 3
> >> 101 20 December 2008 Product 4
> >> 102 10 September 2008 Product 3
> >> 102 12 September 2008 Product 5
> >> 102 20 January 2009 Product 5.....
> >>
> >> Sample rule:
> >> Rule ID Consequent Antecedents                        Support %
> >> Confidence %
> >> Rule 1   Product 4    Product 1 then Product 3        57.1
> >>  75.0
> >>
> >> I want to identify rules such as: after acquiring product 1 and then
> >> product 3, customers have an increased likelihood
> >> (75%) of purchasing product 4 next.
> >>
> >> Thanks,
> >> Nishant
> >>
> >>
> >> On Sun, Nov 27, 2011 at 3:27 PM, Paritosh Ranjan <[EMAIL PROTECTED]>
> >> wrote:
> >> > Can you tell something about your use case?
> >> >
> >> > Paritosh
> >> >
> >> > On 27-11-2011 15:14, Nishant Chandra wrote:
> >> >>
> >> >> Hi,
> >> >>
> >> >> Is there any implementation for Sequential Pattern Mining in Mahout?
> I
> >