Clustering

• US 20030074251A1
• Filed: 10/11/2001
• Published: 04/17/2003
• Est. Priority Date: 10/11/2001
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: ProfitLogic, Inc. (Oracle Corporation)
• Inventors: Kumar, Mahesh, Woo, Jonathan W., Gaidarev, Peter
• Granted Patent: US 6,834,266 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Claims

1. A method comprising

receiving a set of data containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution,

expressing the values for each of the data points in a form that includes information about the distribution of the values for each of the data points, and

using the distribution information in clustering the set of data with at least one other set of data containing values associated with data points.

2. The method of claim 1 in which the respective data points are related in a time-sequence.

3. The method of claim 1 in which the data points relate to a seasonality of at least one item of commerce.

4. The method of claim 1 in which each of the sets of data relates to seasonalities of items of commerce.

5. The method of claim 4 in which the items of commerce comprise retail products, the data points relate to times during a season, and the values associated with each of the data points correspond to respective ones of the retail products.

6. The method of claim 5 also including determining statistical measures of the variability of the values with respect to the data point.

7. The method of claim 1 in which the data is expressed in a form that includes a mean of the values associated with a data point and a statistical measure of the distribution with respect to the mean.

8. The method of claim 7 in which the statistical measure comprises a standard deviation.

9. The method of claim 1 in which the clustering of data includes measuring a distance between pairs of the sets of data.

10. The method of claim 9 in which the distance is measured based on the means and variances at the data points.

11. The method of claim 10 in which the distance is measured based on

d_y=ChiSqr_PDF

12. The method of claim 1 in which the distribution of the values comprises a Gaussian distribution.

13. The method of claim 1 in which the clustering of data includes merging the data sets belonging to a cluster using a weighted average.

14. The method of claim 1 also including merging the seasonalities of the data sets belong to a cluster

15. A machine-accessible medium that when accessed results in a machine effecting actions comprising:

receiving a set of data containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution,

expressing the values for each of the data points in a form that includes information about a distribution of the values for each of the data points, and

using the distribution information in clustering the set of data with at least one other set of data containing values associated with data points.

16. A method comprising

receiving sets of data, each of the sets containing values associated with respect data points, the values associated with each of the data points being characterized by a distribution,

evaluating a distance function that characterizes the similarity or dissimilarity of at least two of the sets of data, the distance function including a factor based on the distributions of the values in the sets, and

using the evaluation of the distance function as a basis for clustering of the sets of data.

17. A method comprising

receiving data that represents seasonality time-series for each of a set of retail items, the data also representing error information associated with data values for each of a series of time points for each of the items, and

forming composite seasonality time-series based on respective clusters of the retail item seasonality time-series, the composites formed based in part on the error information.