I saw your post mid April, hoping that someone would provide some options. I have no turn-key solution, and I am anxiously waiting JMP14.1 updates for text mining.
If I were tasked with a problem similar to what you described, I would break it down into 3 parts:
Classification of purchases. For example TV, speakers, DVR, DiscPlayer(Blue Ray), Couch/lounger... Create codes for the classifications. So Step 1 is classify and code.
Create a table/sequences where each row is a consumer, maybe some columns of demographics if available, then columns for Purchase 1-k and Date 1-k. Step 2 is to build/organize data for analysis.
I'd probably start with something like finding the Longest Common Sequence and create distributions/summaries and use techniques and create plots similar to those used for MDS, multidimensional scaling. See the link:
JMP provides a function called Shortest Edit Distance() and provides an example of Longest Common Sequence. You can do a web search for Longest Common Sequence, LCS. Linear programming or recursion is used to find n. Here are two links if you like to see the details:
The JMP function Shortest Edit Distance() is very powerful, and allows for multiple methods for displaying the results. We created an example script for our book. I did not attach it since I have a feeling you were looking for something more turn-key.
Below is the script copied from the Scripting Index. Try it out for different strings.
Names Default To Here( 1 );
editList = Shortest Edit Script(
"time flies like an arrow",
"fruit flies like a banana"
common = "";
/* assemble a longest common subsequence */
For( i = 1,
i <= N Items( editList ), i++,
If( editList[i] == "Common",
/* or Insert or Remove */
common = common || editList[i] /* the snippet */
This book looked interesting and there seems to be some packages in Python and R to do this type of analysis