Heuristic clustering
Methods and apparatus are disclosed regarding an e-commerce system that places customers into a plurality of clusters and tailors services provided to a customer based on the cluster in which the customer is placed. In one embodiment, the e-commerce system defines the clusters based on purchase history data for customers having sufficient purchase history data. The e-commerce system then places customers without sufficient purchase history data into one of the defined clusters based on demographic data for the customer and demographic data for the customers in the cluster.
1. A method, the method comprising:
splitting a plurality of records into first members and second members, wherein:
each first member has a threshold amount of data, and
each second member lacks the threshold amount of data;
generating a matrix associated with the data of the first group;
applying principal component analysis to the matrix to obtain a reduced matrix for the first group;
generating a plurality of sets of first members according to the reduced matrix;
partitioning each of the plurality of sets of first members to reduce dissimilarities among the first members;
clustering the first members into a plurality of clusters according to the partitioning; and
placing a second member into a particular cluster, of the plurality of clusters, according to demographic data of the second member and demographic data of first members in the particular cluster.
2. The method of claim 1 , comprising validating the plurality of clusters prior to placing the second member into the particular cluster.
3. The method of claim 1 , comprising recalibrating a quantity of dimensions of the reduced matrix until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
4. The method of claim 1 , comprising recalibrating a quantity of clusters used by the clustering until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
5. The method of claim 1 , comprising:
generating silhouette numbers for the first group, and
determining, according to the silhouette numbers for the first group, that the plurality of clusters are a valid clustering of the first members.
6. The method of claim 1 , wherein:
partitioning comprises calculating distances between first members,
a larger distance corresponds to a greater dissimilarity, and
each cluster, in the plurality of clusters, is defined according to a smallest sum of distances.
7. The method of claim 1 , wherein:
partitioning comprises determining a particular first member according to a sum of dissimilarities between the particular first member and all other first members, and
the sum associated with the particular first member is a minimum.
8. A non-transitory computer readable medium, comprising a plurality of instructions, that in response to being executed, result in a computing device:
splitting a plurality of records into first members and second members, wherein:
each first member has a threshold amount of data, and
each second member lacks the threshold amount of data;
generating a matrix associated with the data of the first group;
applying principal component analysis to the matrix to obtain a reduced matrix for the first group;
generating a plurality of sets of first members according to the reduced matrix;
partitioning each of the plurality of sets of first members to reduce dissimilarities among the first members;
clustering the first members into a plurality of clusters according to the partitioning; and
placing a second member into a particular cluster, of the plurality of clusters, according to demographic data of the second member and demographic data of first members in the particular cluster.
9. The non-transitory computer readable medium of claim 8 , wherein in response to being executed, the plurality of instructions result in the computing device validating the plurality of clusters prior to placing the second member into the particular cluster.
10. The non-transitory computer readable medium of claim 8 , wherein in response to being executed, the plurality of instructions result in the computing device recalibrating a quantity of dimensions of the reduced matrix until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
11. The non-transitory computer readable medium of claim 8 , wherein in response to being executed, the plurality of instructions result in the computing device recalibrating a quantity of clusters used by the clustering until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
12. The non-transitory computer readable medium of claim 8 , wherein in response to being executed, the plurality of instructions result in the computing device:
generating silhouette numbers for the first group, and
determining, according to the silhouette numbers for the first group, that the plurality of clusters are a valid clustering of the first members.
13. The non-transitory computer readable medium of claim 8 , wherein:
partitioning comprises calculating distances between first members,
a larger distance corresponds to a greater dissimilarity, and
each cluster, in the plurality of clusters, is defined according to a smallest sum of distances.
14. The non-transitory computer readable medium of claim 8 , wherein:
partitioning comprises determining a particular first member according to a sum of dissimilarities between the particular first member and all other first members, and
the sum associated with the particular first member is a minimum.
15. A system, the system comprising:
one or more processors configured to:
split a plurality of records into first members and second members, wherein:
each first member has a threshold amount of data, and
each second member lacks the threshold amount of data;
generate a matrix associated with the data of the first group;
apply principal component analysis to the matrix to obtain a reduced matrix for the first group;
generate a plurality of sets of first members according to the reduced matrix;
partition each of the plurality of sets of first members to reduce dissimilarities among the first members;
cluster the first members into a plurality of clusters according to the partitioning; and
place a second member into a particular cluster, of the plurality of clusters, according to demographic data of the second member and demographic data of first members in the particular cluster.
16. The system of claim 15 , wherein the one or more processors are configured to validate the plurality of clusters prior to placing the second member into the particular cluster.
17. The system of claim 15 , wherein the one or more processors are configured to recalibrate a quantity of dimensions of the reduced matrix until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
18. The system of claim 15 , wherein the one or more processors are configured to recalibrate a quantity of clusters used by the clustering until a cluster validation determines the plurality of clusters are a valid clustering of the first members.
19. The system of claim 15 , wherein the one or more processors are configured to:
generate silhouette numbers for the first group, and
determine, according to the silhouette numbers for the first group, that the plurality of clusters are a valid clustering of the first members.
20. The system of claim 15 , wherein:
partitioning comprises calculating distances between first members,
a larger distance corresponds to a greater dissimilarity, and
each cluster, in the plurality of clusters, is defined according to a smallest sum of distances.
21. The system of claim 15 , wherein:
partitioning comprises determining a particular first member according to a sum of dissimilarities between the particular first member and all other first members, and
the sum associated with the particular first member is a minimum.