Data classifier for classifying pattern data into clusters
First Claim
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1. A data classifier for classifying a plurality of input pattern data into one or more clusters, whereininitially, for each pattern data, a cluster to which the pattern data belongs is provisionally determined;
- for each cluster, a predetermined correlation value is calculated between one or more pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster; and
a cluster to which the observational pattern data belongs is determined based on the correlation value for each cluster.
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Abstract
A data classifier classifies a plurality of input pattern data into one or more clusters. For each pattern data, a cluster to which the pattern data belongs is provisionally determined. For each cluster, a predetermined correlation value is calculated between one or more pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster. A cluster to which the observational pattern data belongs is determined based on the correlation values.
20 Citations
13 Claims
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1. A data classifier for classifying a plurality of input pattern data into one or more clusters, wherein
initially, for each pattern data, a cluster to which the pattern data belongs is provisionally determined; -
for each cluster, a predetermined correlation value is calculated between one or more pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster; and a cluster to which the observational pattern data belongs is determined based on the correlation value for each cluster.
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2. A data classifier for classifying a plurality of input pattern data into one or more clusters, wherein
initially, for each pattern data, a cluster to which the pattern data belongs is provisionally determined; -
for each cluster, each degree of similarity between each pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster is determined using a predetermined function and a correlation value is calculated by summing the values of each degree of similarity; and a cluster to which the observational pattern data belongs is determined based on the correlation value determined for each cluster. - View Dependent Claims (3, 4)
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5. A data classifier for classifying a plurality of input pattern data into one or more clusters, wherein
(a) for each pattern data, a cluster to which the pattern data belongs is provisionally determined; -
(b) each pattern data is sequentially selected as observational pattern data which is to become a target to be classified into a cluster; (c) for each cluster, a predetermined correlation value is calculated between one or more pattern data belonging to the cluster and the observational pattern data which is a target to be classified into a cluster; (d) a cluster to which the observational pattern data should belong is determined based on the correlation value for each cluster; and the processes of (b), (c), and (d) are repeated until there is no change in the cluster to which each pattern data should belong, and each pattern data is classified into a cluster.
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6. A data classification method for classifying a plurality of input pattern data into one or more clusters, comprising the steps of:
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provisionally determining, for each pattern data, a cluster to which the pattern data belongs; calculating, for each cluster, a predetermined correlation value between one or more pattern data belonging to the cluster and observational pattern data which is a target to be classified; and determining a cluster to which the observational pattern data belongs based on the correlation value for each cluster.
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7. A data classification method for classifying a plurality of input pattern data into one or more clusters, comprising the steps of:
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provisionally determining, for each pattern data, a cluster to which the pattern data belongs; for each cluster, determining, using a predetermined function, a degree of similarity between each pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster and calculating a correlation value by summing the values of the degree of similarity; and determining a cluster to which the observational pattern data belongs based on the correlation value for each cluster. - View Dependent Claims (8, 9)
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10. A data classification program embodied on a computer-readable medium which, when executed, causes a computer to classify a plurality of input pattern data into one or more clusters by executing the steps of:
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provisionally determining, for each pattern data, a cluster to which the pattern data belongs; calculating, for each cluster, a predetermined correlation value between one or more pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster; and determining a cluster to which the observational pattern data belongs based on the correlation value for each cluster.
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11. A data classification program embodied on a computer-readable medium which, when executed, causes a computer to classify a plurality of input pattern data into one or more clusters by executing the steps of:
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provisionally determining, for each pattern data, a cluster to which the pattern data belongs; for each cluster, determining, using a predetermined function, a degree of similarity between each pattern data belonging to the cluster and observational pattern data which is a target to be classified into a cluster and calculating a correlation value by summing the values of the degree of similarity; and determining a cluster to which the observational pattern data belongs based on the correlation value of each cluster. - View Dependent Claims (12, 13)
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Specification