Data classifier using learning-formed and clustered map

• US 20030158828A1
• Filed: 01/30/2003
• Published: 08/21/2003
• Est. Priority Date: 02/05/2002
• Status: Active Grant

• Current Assignee: Fuji Xerox Company Limited (Xerox Holdings Corp.)
• Original Assignee: Fuji Xerox Company Limited (Xerox Holdings Corp.)
• Inventors: Ikeda, Hitoshi, Kato, Noriji, Kashimura, Hirotsugu
• Granted Patent: US 7,120,614 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06N 3/02 Neural networks

Abstract

A data classifier performs a data classification process using prototypes classified into clusters. A prototype map is formed using mapping means and clustering means. The mapping means forms, through learning, a prototype map by adjusting coupling weights between a plurality of prototypes provided in a map space based on a plurality of input data. The clustering means calculates a predetermined measure between the prototypes and classifies the prototypes into a plurality of clusters based on the measure.

Claims

1. A data classifier for classifying input data using a result of a learning process based on the input data and using a self-organizing map, the classifier comprising:

means for forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters; and

means for evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the result of the evaluation, wherein

a learning formation process of a self-organizing map is again performed using the adjusted learning parameters.

2. A data classifier comprising:

mapping means for adjusting coupling weights between a plurality of prototypes in a map space using a plurality of input data and forming a prototype map through learning; and

clustering means for calculating a predetermined measure defined between each prototype and classifying the prototypes into a plurality of clusters based on the measure, wherein

the prototype map classified into clusters is supplied for a data classification process.

3. A data classifier according to claim 2, wherein

the clustering means provisionally determines clusters to which each prototype belongs;

the clustering means calculates, for each cluster, a measure between at least one prototype belonging to the cluster and an observatory prototype to be classified into a cluster, and

the clustering means determines the cluster to which the observatory prototype belongs based on the calculated measure.

4. A data classifier according to claim 2, wherein

(a) the clustering means provisionally determines clusters to which the prototypes belong;

(b) the clustering means sequentially selects each prototype as an observatory prototype to be classified into a cluster;

(c) the clustering means calculates, for each cluster, a measure between at least one prototype belonging to the cluster and the observatory prototype;

(d) the clustering means changes, as necessary, the cluster to which the observatory prototype belongs based on the calculated measure, and

the processes of (b), (c), and (d) are repeated until there is no change in the cluster to which each prototype belongs so that each prototype is classified into a cluster.

5. A data classifier according to claim 3, wherein

a parameter used for the measure calculation is determined based on a distribution of an occurrence frequency of a degree of similarity between prototypes.

6. A data classifier according to claim 2, wherein

the learning formation of prototype map is achieved through a self-organizing mapping.

7. A data classifier according to claim 2 further comprising:

means for forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters, and

means for evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the result of the evaluation, wherein

a learning formation of a self-organizing map is again performed using the adjusted learning parameters.

8. A data classifier according to claim 7, wherein

the evaluation of the self-organizing maps is performed based on a phase relationship between a feature space for input data and a prototype map space and on an average quantization error of the prototype map.

9. A data classifier according to claim 2, wherein

the classifier further comprises means for forming, after each prototype is classified into a cluster, at least one new prototype in a boundary section of clusters, and

execution of a further classification process of the new prototype into a cluster is enabled.

10. A data classifier according to claim 9, wherein

when the new prototype is formed, an additional learning process is executed for the new prototype.

11. A data classifier according to claim 9, wherein

the new prototype is formed based on a statistical calculation for prototypes present in the neighborhood of the boundaries.

12. A data classifier according to claim 9, wherein

the new prototype is formed in portions wherein a density of input data referred to by the prototypes is lower than a predetermined threshold value.

13. A data classifier according to claim 9, wherein

the new prototype is formed in a boundary section of clusters and in portions wherein a density of input data referred to by the prototypes present in that section is lower than a predetermined threshold value.

14. A data classifier according to claim 2, wherein

the measure is calculated as a value of a function which rapidly and asymptotically approaches 0 from a predetermined differential value as the degree of similarity between prototypes decreases, and

the cluster to which the observatory prototype belongs is determined using the measure obtained as the value of the function.

15. A method for classifying data by executing a learning process based on input data using a self-organizing map and classifying data using the learning result, the method comprising the steps of:

forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters; and

evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the evaluation result, wherein

a self-organizing map is again formed through learning with the adjusted learning parameters.

16. A method for classifying data, comprising:

a mapping step for forming, through learning, a prototype map by adjusting coupling weights between a plurality of prototypes in a map space according to a plurality of data inputs, and

a clustering step for calculating a predetermined measure defined between each prototype and classifying the prototypes into a plurality of clusters based on the measure, wherein

the prototype map classified into clusters is supplied for a data classification process.

17. A method for classifying data according to claim 16, further comprising the step of

forming, after each prototype is classified into a cluster, at least one new prototype in a cluster boundary.

18. A method for classifying data according to claim 16, wherein

in the mapping step, a plurality of learning parameters are used for the learning formation of a plurality of prototype maps corresponding to the learning parameters, each of the plurality of learning-formed prototype maps is evaluated, the learning parameters are adjusted based on the evaluation result, and the learning formation of a prototype map is again performed using the adjusted learning parameters.

19. A method for classifying data according to claim 18, wherein

the evaluation of maps is executed based on a phase relationship between a feature space for the input data and a prototype map space and on an average quantization error of the prototype map.

20. A data classifying program which instructs a computer to execute a process to perform learning based on input data using a self-organizing map and to classify data using the results of the learning, the program instructing a computer to execute the steps of:

forming, through learning, a plurality of self-organizing maps corresponding to a plurality of learning parameters; and

evaluating each of the plurality of learning-formed self-organizing maps and adjusting the learning parameters based on the evaluation results, wherein

a learning formation process for a self-organizing map is again performed with the adjusted learning parameters.

21. A data classifying program which instructs a computer to execute the steps of:

a mapping procedure for adjusting coupling weights between a plurality of prototypes in a map space using a plurality of input data and for forming, through learning, a prototype map, and

a clustering procedure for calculating a predetermined measure defined between each prototype and for classifying the prototypes into a plurality of clusters based on the measure, wherein

the prototype map classified into clusters is supplied for a data classification process.

22. A data classifying program according to claim 21, further instructing a computer to execute the step of

a procedure for forming, after each prototype is classified into a cluster, at least one new prototype in a cluster boundary.

23. A data classifying program according to claim 21, wherein

in the mapping procedure, a plurality of learning parameters are used to form, through learning, a plurality of prototype maps each corresponding to each learning parameter, each of the plurality of learning-formed prototype maps is evaluated, the learning parameters are adjusted based on the result of evaluation, and the learning formation of a prototype map is again performed with the adjusted learning parameters.

24. A data classifying program according to claim 23, wherein

the map evaluation is performed based on a phase relationship between a feature space for input data and a prototype map space and on an average quantization error of the prototype map.