A data classification method which classifies a plurality of data into a plurality of classification items based on a feature quantity included in the data, the method includes calculating, by a processor, an appearance probabilities in which training data including the feature quantity appears in the classification items in a distribution of the data, generating, by the processor, a rule having the feature quantity and a weighting of the feature quantity based on a plurality of the training data having the feature quantity based on the appearance probabilities; and classifying, by the processor, the plurality of data according to the rule.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data classification method which classifies a plurality of data into a plurality of classification items based on a feature quantity included in the data, the method comprising: calculating, by a processor, an appearance probabilities in which training data including the feature quantity appears in the classification items in a distribution of the data; generating, by the processor, a rule having the feature quantity and a weighting of the feature quantity based on a plurality of the training data having the feature quantity based on the appearance probabilities; and classifying, by the processor, the plurality of data according to the rule. 2. The data classification method according to claim 1, wherein the calculating comprises calculating the appearance probabilities, based on a first ratio of the plurality of the classification items in the data, a second ratio of the plurality of the classification items in the plurality of the training data, and a ratio of the training data including the feature quantity in each of the plurality of classification items in the plurality of the training data. 3. The data classification method according to claim 1, wherein the generating comprises: determining whether or not a value of the feature quantity in the plurality of the training data is used based on the appearance probabilities of the feature quantity; and generating the rule having the feature quantity and the weighting of the feature quantity based on the feature quantity which is determined to use. 4. The data classification method according to claim 3, wherein the determining comprises: first determining whether or not the value of the feature quantity in the training data belonging to a first classification item in the plurality of the training data is used based on the appearance probability of the first classification item of the feature quantity; and second determining whether or not the value of the feature quantity in the training data belonging to a second classification item in the plurality of the training data is used based on the appearance probability of the second classification item of the feature quantity. 5. The data classification method according to claim 1, wherein the method further comprising repeating the generating until a precision of a classification result to the classification items of the plurality of the training data based on the rule which is generated reaches a predetermined standard. 6. The data classification method according to claim 5, wherein the generating comprising generating the rule based on the value of the feature quantity which is once obtained based on the appearance probabilities of the feature quantity, when repeatedly using the plurality of the training data. 7. The data classification method according to claim 5, wherein the generating comprising generating the rule based on the value of the feature quantity which is obtained every time based on the appearance probabilities of the feature quantity, when repeatedly using the plurality of the training data. 8. A non-transitory computer readable storage medium storing therein a program for causing a computer to execute a process, the process comprising: calculating an appearance probabilities in which training data including a feature quantity appears in a classification items in a distribution of the data; and generating a rule which classifies the plurality of data into the plurality of classification items and has the feature quantity and a weighting of the feature quantity based on a plurality of the training data having the feature quantity based on the appearance probabilities. 9. The non-transitory computer readable storage medium according to claim 8, wherein the calculating comprises calculating the appearance probabilities, based on a first ratio of the plurality of the classification items in the data, a second ratio of the plurality of the classification items in the plurality of the training data, and a ratio of the training data including the feature quantity in each of the plurality of classification items in the plurality of the training data. 10. The non-transitory computer readable storage medium according to claim 8, wherein the generating comprises: determining whether or not a value of the feature quantity in the plurality of the training data is used based on the appearance probabilities of the feature quantity; and generating the rule having the feature quantity and the weighting of the feature quantity based on the feature quantity which is determined to use. 11. The non-transitory computer readable storage medium according to claim 10, wherein the determining comprises: first determining whether or not the value of the feature quantity in the training data belonging to a first classification item in the plurality of the training data is used based on the appearance probability of the first classification item of the feature quantity; and second determining whether or not the value of the feature quantity in the training data belonging to a second classification item in the plurality of the training data is used based on the appearance probability of the second classification item of the feature quantity. 12. The non-transitory computer readable storage medium according to claim 8, wherein the method further comprising repeating the generating until a precision of a classification result to the classification items of the plurality of the training data based on the rule which is generated reaches a predetermined standard. 13. A classification device comprising: a memory which stores a plurality of data for classification; and a processor configured to execute a process, the process including: calculating an appearance probabilities in which training data including a feature quantity appears in a classification items in a distribution of the data; and generating a rule which classifies the plurality of data into the plurality of classification items and has the feature quantity and a weighting of the feature quantity based on a plurality of the training data having the feature quantity based on the appearance probabilities. 14. The classification device according to claim 13, wherein the processor calculates the appearance probabilities, based on a first ratio of the plurality of the classification items in the data, a second ratio of the plurality of the classification items in the plurality of the training data, and a ratio of the training data including the feature quantity in each of the plurality of classification items in the plurality of the training data in the memory. 15. The classification device according to claim 13, wherein the processor determines whether or not a value of the feature quantity in the plurality of the training data is used based on the appearance probabilities of the feature quantity, and generates the rule having the feature quantity and the weighting of the feature quantity based on the feature quantity which is determined to use. 16. The classification device according to claim 15, wherein the processor determines whether or not the value of the feature quantity in the training data belonging to a first classification item in the plurality of the training data is used based on the appearance probability of the first classification item of the feature quantity, and determines whether or not the value of the feature quantity in the training data belonging to a second classification item in the plurality of the training data is used based on the appearance probability of the second classification item of the feature quantity. 17. The classification device according to claim 13, wherein the processor repeats the generating until a precision of a classification result to the classification items of the plurality of the training data based on the rule which is generated reaches a predetermined standard.