Abstract: According to an aspect of the present invention, there is provided with a data division apparatus which divides multi-dimensional data, including: a data input unit which inputs multi-dimensional data; a division plane candidate creator which creates a plurality of division plane candidates for dividing the multi-dimensional data; a data provisional division unit which provisionally divides the multi-dimensional data by using the division plane candidate to generate clusters; a model generator which generates models from the clusters; an evaluation value calculator which calculates an evaluation value on the basis of the generated models and the multi-dimensional data; a division candidate selector which compares evaluation values respectively corresponding to the division plane candidates and selects a division plane candidate having a highest evaluation value; and a data division unit which divides the multi-dimensional data by using the selected division plane candidate.

Abstract: There is provided with a data analysis method including: reading out, from a database which is a set of records each including plural explanation variables and a target variable, the target variables of the records; generating a first plurality of clusters based on the read target variables of the records; determining to which cluster each record belongs; generating a classification rule for predicting a cluster from explanation variables; storing the generated classification rule; selecting an explanation variable referred to in the generated classification rule; storing the selected explanation variable in an explanation variable list; and generating a second plurality of clusters based on explanation variables in the records on the explanation variable list and the target variables of the records.

Abstract: There is provided a data processing apparatus including: a classification rule generation unit that generates a classification rule having a plurality of partial rules, using a set of records each record including a plurality of attribute values; a partial rule selection unit that selects a partial rule whose classification accuracy does not satisfy a predetermined standard; a record detection unit that detects records which accord with a conditional part of the selected partial rule from among the set of records; an additional attribute decision unit that decides a additional attribute to be newly added; a retrieval request unit that requests a retrieval system to retrieve attribute values of the detected records for the additional attribute; and a partial rule regeneration unit that regenerates a partial rule for replacing the selected partial rule, using the attribute values for the additional attribute retrieved by the retrieval system.

Abstract: An inverse model calculation apparatus and method according to an embodiment of the present invention record an input value inputted sequentially to a target system and an output value outputted sequentially from the target system as time series data, generate a decision tree for inferring an output value at future time, using the time series data, detect a leaf node having an output value at future time as a value of an object variable from the decision tree, and acquire a condition of explaining variables included in a rule associated with a path from a root node of the decision tree to the detected leaf node, as a condition for obtaining the output value.

Abstract: An inference apparatus predicts the class of a new instance whose class is unknown or generates a rule which is common to an instance set. In the inference apparatus, an instance memory section stores the instance set which each include attribute--value and class. The data type of the attribute--value is discrete type or continuous type. An instance expression section converts the attribute--value of discrete type to a first data expression and the attribute--value of continuous type to a second data expression. A parameter calculation section generates a linear function of each class in which the attribute--value is a variable and calculates a coefficient of each variable in the linear function by assigning one of the first data expression and the second data expression to the variables.