Clustering customers
A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the tune attribute.
1. A system for clustering customers, comprising:
a receiving device, configured to receive a set of customer records, wherein each customer record in the set of customer records represents one customer, each customer record includes at least one data attribute, and each data attribute has a data attribute value;
a pre-processing device, configured to pre-process the set of customer records to generate a pre-processed set of customer records;
wherein, to pre-process the set of customer records, the pre-processing device is further configured to:
determine the type of the customer represented by each record in the set of customer records;
use a type attribute to represent the type of the customer in the corresponding customer record, wherein the type attribute indicates whether the corresponding customer is a seed customer or a non-seed customer;
normalize the data attribute values and the type attribute values; and
weight the data attribute values and the type attribute values, wherein the weighting comprises multiplying the data attribute values by a dispersion weighting factor and multiplying the type attribute values by a purity weighting factor;
a computer processor, configured to execute a clustering algorithm on the pre-processed set of customer records to cluster the pre-processed set of customer records into a pre-defined number of clusters, wherein each of the clusters comprises two or more customer records representing two or more customers, and wherein each pre-processed customer record in the pre-processed set of customer records being clustered comprises at least a normalized and weighted data attribute and a normalized and weighted type attribute;
wherein the dispersion weighting factor used to weight the data attribute values and the purity weighting factor used to weight the type attribute values are adjustable to affect the dispersion and purity of a clustering result of the clustering algorithm applied to the set of customer records.
2. The system of claim 1 , wherein the sum of the dispersion weighting factor used to weight the data attribute values and the purity weighting factor used to weight the type attribute values is 1.
3. The system of claim 1 , wherein the computer processing device is configured to divide the customer records into the current set of clusters by using a K-means clustering algorithm.
4. The system of claim 3 , further comprising:
a mechanism configured to select clusters with higher purity from the pre-defined number of clusters, and to output data attribute values of customer records in the clusters, wherein the purity of a cluster is the ratio of the number of customer records with a specified type attribute in the cluster to the total number of customer records in the cluster.