JP2019008640A - Similarity calculating device and similarity calculating method - Google Patents

Abstract

To provide a similarity calculating device capable of calculating an inter-case similarity and an inter-attribute similarity according a uniform standard and efficiently, based on a plurality of cases and data showing that each of the plurality of cases has a plurality of attribute values.SOLUTION: The similarity calculating device according to an embodiment includes a classifier generation unit. For each attribute, the classifier generation unit generates, from input data composed of a set of a plurality of cases, a plurality of attributes respectively associated with the plurality of cases, and a class including positive cases and negative cases, a classifier that outputs a classification score from the category data based on a ratio of the positive cases included in the class and a ratio of positive cases of predetermined category data included in a predetermined attribute.SELECTED DRAWING: Figure 3

Description

  Embodiments relate to a similarity calculation device and a similarity calculation method.

  Conventionally, a plurality of cases and a similarity calculation device for calculating the similarity between cases and the similarity between attributes from data in which each of the plurality of cases has a plurality of attribute values have been proposed. In the similarity calculation device, when all the attributes are numerical data and there is no extreme bias in the distribution of each attribute, processing by classical statistics is possible, and the correlation coefficient between the row vectors, and By calculating the correlation coefficient between column vectors, the similarity between cases and the similarity between attributes can be calculated.

  As a similarity calculation method for more general data in which numerical data and discrete category data are mixed, a method using machine learning is also being studied. In the technique of Patent Document 1, the frequency at which the condition part and the conclusion part occur at the same time is obtained by correlation rule analysis for data having attribute sets that are correlated with each other, and the similarity is corrected based on the result. A similar case that follows the user's intuition is output.

  However, in calculating the similarity between cases, the user needs to set the attribute weight and the similarity between attribute values in advance.

  In the technique of Patent Document 2, a subset of cases is randomly extracted, repeatedly classified using a decision tree or the like, and the degree of similarity between cases is calculated by counting whether the classification result classes between cases are the same. To do. However, the calculation efficiency is poor because a large number of iterative processes are required, and the calculation result of the similarity between cases may greatly change depending on the result of random extraction.

JP 2002-149697 A Japanese Patent No. 5391637

  The problem of the embodiment is to efficiently calculate the similarity between cases and the similarity between attributes from a plurality of cases and data in which each of the plurality of cases has a plurality of attribute values in a unified standard. It is an object of the present invention to provide a similarity calculation device and a similarity calculation method that can be used.

  The similarity calculation apparatus according to the embodiment includes a classifier generation unit. The classifier generator generates positive data included in a class from input data composed of a plurality of cases, a plurality of attributes associated with each of the plurality of cases, and a class including positive examples and negative examples. Based on the ratio of examples and the ratio of positive examples of predetermined category data included in predetermined attributes, a classifier that outputs a classification score from category data is generated for each attribute.

It is a figure which shows an example of a structure of the similarity calculation system which concerns on embodiment. It is a figure which shows an example of a structure of a similarity calculation apparatus. It is a figure which shows an example of the similarity calculation process part comprised by the similarity calculation apparatus. It is a figure which shows an example of the input data. 5 is a flowchart illustrating an example of processing of a classifier generation unit 32. It is a figure which shows an example of the classifier 42 produced | generated by the process of the classifier production | generation part 32. FIG. It is a flowchart which shows an example of a process of the evaluation part. It is a figure which shows an example of the classification score matrix 43 produced | generated by the process of the evaluation part 33. FIG. 5 is a flowchart showing an example of processing of a case similarity calculation unit 34. It is a figure which shows an example of the similarity matrix 44 between cases produced | generated by the process of the similarity calculation part 34 between cases. 5 is a flowchart illustrating an example of processing of an attribute similarity calculation unit 35. It is a figure which shows an example of the similarity matrix 45 between attributes produced | generated by the process of the similarity calculation part 35 between attributes.

  Hereinafter, embodiments will be described in detail with reference to the drawings.

  1 is a diagram illustrating an example of a configuration of a similarity calculation system according to the embodiment, FIG. 2 is a diagram illustrating an example of a configuration of a similarity calculation device, and FIG. 3 is a configuration of the similarity calculation device. It is a figure which shows an example of the similarity calculation process part.

  As shown in FIG. 1, the similarity calculation system 1 includes a similarity calculation device 11 that is a personal computer or the like operated by a user, and a database server 12 connected to the similarity calculation device 11 via a network NW. It is configured.

  As shown in FIG. 2, the similarity calculation device 11 includes a control unit 21, a ROM 22, a RAM 23, an input device 24, an output device 25, a communication interface (hereinafter referred to as communication IF) 26, and a storage medium attachment / detachment. The unit 27 and the bus 28 are included. The control unit 21, ROM 22, RAM 23, input device 24, output device 25, communication IF 26, and storage medium attaching / detaching unit 27 are connected to each other via a bus 28.

  The ROM 22 stores a similarity calculation program for calculating the similarity of data. The control unit 21 reads out the similarity calculation program stored in the ROM 22 and develops it on the RAM 23 for execution. Thereby, a similarity calculation processing unit 31 shown in FIG. 3 to be described later is configured.

  The input device 24 is configured to be able to input information, instructions, and the like according to user operations. Specifically, the input device 24 includes, for example, at least one of a keyboard, a mouse, and a touch panel.

  The output device 25 is configured to output information generated according to the processing of the control unit 21 to the outside. Specifically, the output device 25 includes, for example, a display or a printer.

  The communication IF 26 is configured by a device capable of transmitting / receiving data and the like to / from the database server 12 via the network NW. The communication IF 26 of the similarity calculation device 11 is configured such that input data 41 (see FIG. 3) is input from the database server 12 via the network NW.

  The storage medium attaching / detaching unit 27 is configured so that a storage medium such as a memory card or a USB memory can be attached / detached. The input data 41 (see FIG. 3) may be input from a storage medium attached to the storage medium attaching / detaching unit 27.

  As shown in FIG. 3, the similarity calculation processing unit 31 includes a classifier generation unit 32, an evaluation unit 33, an inter-case similarity calculation unit 34, and an attribute similarity calculation unit 35. ing.

  The classifier generation unit 32 generates a classifier 42 that returns a numerical value based on likelihood for each attribute from the input data 41. The classifier generation unit 32 outputs the generated classifier 42 to the evaluation unit 33. More specifically, the classifier generation unit 32 is a set of a plurality of cases X1 to X10, a plurality of attributes Y1 to Y3 associated with each of the plurality of cases, and a class including a positive example and a negative example. Classifier Z1 that outputs a classification score from category data based on the ratio of positive examples included in a class and the ratio of positive examples of predetermined category data included in predetermined attributes ~ Z3 are generated for each of the attributes Y1 to Y3.

  The evaluation unit 33 outputs a classification score based on the input data 41 by the classifier 42 and generates a classification score matrix 43. That is, the evaluation unit 33 processes the input data 41 by the classifier 42 and generates a classification score matrix 43. The evaluation unit 33 outputs the generated classification score matrix 43 to the inter-case similarity calculation unit 34 and the inter-attribute similarity calculation unit 35.

  The inter-case similarity calculation unit 34 generates an inter-case similarity matrix 44 based on the classification scores of the attributes Y1 to Y3 associated with the respective cases X1 to X10 from the classification score matrix 43.

  The inter-attribute similarity calculation unit 35 generates an inter-attribute similarity matrix 45 based on the classification scores of the cases X1 to X10 associated with the respective attributes Y1 to Y3 from the classification score matrix 43.

  Next, a specific example of processing according to the embodiment will be described with reference to FIGS.

  4 is a diagram illustrating an example of the input data 41, FIG. 5 is a flowchart illustrating an example of the processing of the classifier generation unit 32, and FIG. 6 is a classification generated by the processing of the classifier generation unit 32. FIG.

  As shown in FIG. 4, the first line (first line) of the input data 41 represents a label, and is formed by a plurality of attributes Y1 to Y3 and one class. In the present embodiment, the input data 41 has three attributes Y, an attribute Y1, an attribute Y2, and an attribute Y3.

  The second and subsequent lines of the input data 41 represent a plurality of cases X1 to X10, and each case X1 to X10 is composed of a set of attributes Y1 to Y3 indicating the respective characteristics and a class value indicating the type of data. Yes. In this embodiment, the input data 41 has ten cases X of cases X1 to X10. In addition, “-” in the attribute represents data loss.

  In the present embodiment, the attribute Y is all category data that is discrete data that is not numerical data. For example, the attribute Y1 of the case X1 is “A”, the attribute Y2 of the case X1 is “C”, and the attribute Y3 of the case X1 is “F”. When the attribute Y includes numerical data, the classifier generation unit 32 converts the numerical data into category data by discretization. For discretization, for example, a known method such as equal interval division or equal frequency division may be used.

  In addition, the class of the input data 41 is a binary value of a positive example (TRUE) and a negative example (FALSE). When the class is multi-valued, the classifier generation unit 32 converts it into binary data, for example, regarding one value as a positive example and the remaining value as a negative example. The positive example and the negative example of the class are set in advance by the user according to the category data of the attributes Y1 to Y3.

  The input data 41 illustrated in FIG. 4 is input to the classifier generation unit 32 and the evaluation unit 33. As described above, the input data 41 is input to the classifier generation unit 32 and the evaluation unit 33 from the database server 12 via the network NW or from a storage medium attached to the storage medium attachment / detachment unit 27. The classifier generation unit 32 generates classifiers Z1 to Z3 using the input data 41 as teacher data.

  In step S <b> 1, the classifier generation unit 32 substitutes 1 for a variable i, and obtains the total number of attributes N and the ratio Pt of positive examples in all cases X. In the present embodiment, since the input data 41 has attributes Y1, Y2, and Y3, the total number of attributes N is “3”. Moreover, since the input data 41 has ten cases X of cases X1 to X10, and four cases X of cases X1 to X4 are positive examples, the ratio Pt of the positive examples in all the cases X is , “4/10”.

  Next, in step S2, the classifier generation unit 32 substitutes 1 for the variable j to obtain the total category number M of the attribute Yi. Since the category data of the attribute Y1 is “A” and “B”, the total category number M is “2”.

  Next, in step S3, the classifier generation unit 32 obtains a positive example ratio P (i, j) of the attribute Yi (i = 1) and the category j (j = 1). Here, if the category j (j = 1) is the category data “A”, the number of category data “A” of the attribute Y1 is 7, of which the number of positive examples is 2. Therefore, the positive example (i, j) of the attribute Yi and category j is “2/7”.

Next, the classifier generation unit 32 generates a classifier Zi having the following properties in step S4.
If Pt <P (i, j), return a positive number. If Pt> P (i, j), return a negative number. Pt = P (i, j) or if missing. That is, if the ratio P (i, j) of a positive example of a certain category j is larger than the ratio Pt of positive examples in all cases X, “if attribute Yi has a value of category j, a positive example The likelihood (likelihood) is higher ”and returns a positive number.

  On the other hand, when the ratio P (i, j) of a positive example of a certain category j is smaller than the ratio Pt of positive examples in all cases X, “if attribute Yi has a value of category j, It returns a negative number.

  Furthermore, when the ratio P (i, j) of positive examples in a certain category j is equal to the ratio Pt of positive examples in all cases X, or when the attribute Yi is missing, “the likelihood of being a positive example ( (Probability) does not change "and returns 0.

As a classifier having such properties, for example, a classifier of the following (formula 1) is used.
log ₁₀ P (i, j) −log ₁₀ Pt (Equation 1)
Here, since the ratio P (i, j) of the positive examples of the attribute Yi and category j is “2/7”, and the ratio Pt of the positive examples in all the cases X is “4/10”. The ratio P (i, j) of positive examples in a certain category j is smaller than the ratio Pt of positive examples in all cases X. Therefore, the classifier Zi (i = 1) returns a negative numerical value. In the following description, the numerical value returned by the classifier Zi, that is, the return value is referred to as “classification score”. By substituting the attribute Yi, the ratio P (i, j) of positive examples in category j and the ratio Pt of positive examples in all cases X into (Equation 1), the classification score is “−0.146”. Become.

  Next, the classifier generation unit 32 increments the variable j by 1 in step S5, and determines whether or not the variable j is larger than the total category number M of the attribute Yi in step S6.

  Here, since the variable j is 2 and the total category number M of the attribute Yi (i = 1) is 2, the classifier generation unit 32 determines that the variable j is not larger than the total category number M of the attribute Yi. Returning to step S3, the same processing is repeated.

  In step S3, the classifier generation unit 32 obtains a positive example ratio P (i, j) of the attribute Yi (i = 1) and the category j (j = 2). Here, if the category j (j = 2) is the category data “B”, the number of category data “B” of the attribute Y1 is 3, and the number of positive examples is 2. Therefore, the ratio P (i, j) of the positive example of the attribute Yi and the category j is “2/3”.

  The ratio P (i, j) of the positive example of the attribute Yi and the category j is “2/3”, and the ratio Pt of the positive example in all the cases X is “4/10”. The ratio P (i, j) of the positive examples of the positive examples is larger than the ratio Pt of the positive examples in all the cases X.

  In step S4, the classifier generator 32 generates a classifier Zi that returns a positive numerical value. By substituting the positive example ratio P (i, j) of the attribute Yi and the category j and the positive example ratio Pt in all the cases X into (Expression 1), the classification score becomes “0.222”. .

  By such processing, as shown in FIG. 6, a classifier Z1 corresponding to the attribute Y1 is generated. The classifier Z1 is configured to return a numerical value (classification score) corresponding to the category data of the attribute Y1.

  Next, the classifier generation unit 32 increments the variable j by 1 in step S5, and determines whether or not the variable j is larger than the total category number M of the attribute Yi in step S6.

  Here, since the variable j is 3 and the total category number M of the attribute Yi (i = 1) is 2, the classifier generation unit 32 determines that the variable j is larger than the total category number M of the attribute Yi, and the step The process proceeds to S7.

  The classifier generation unit 32 increments the variable i by 1 in step S7, and determines whether or not the variable i is greater than the total number of attributes N in step S8.

  Here, since the variable i is 2 and the total number of attributes N is 3, the classifier generation unit 32 determines that the variable i is not greater than the total number of attributes N, returns to step S2, and repeats the same processing.

  Since the category data of the attribute Yi (i = 2) is “C”, “D”, “E”, and “missing”, the total number of categories M is “4”. The classifier generation unit 32 generates the classifier Z2 illustrated in FIG. 6 by repeating the same process as described above for the attribute Y2. The classifier Z2 is configured to return a numerical value (classification score) corresponding to the category data of the attribute Y2.

  Then, the classifier generation unit 32 increments the variable i by 1 in step S7, and determines whether or not the variable i is larger than the total attribute number N in step S8.

  Here, since the variable i is 3 and the total number of attributes N is 3, the classifier generation unit 32 determines that the variable i is not greater than the total number of attributes N, returns to step S2, and repeats the same processing.

  Since the category data of the attribute Yi (i = 3) is “F”, “G”, and “missing”, the total number of categories M is “3”. The classifier generation unit 32 generates the classifier Z3 illustrated in FIG. 6 by repeating the same process as described above for the attribute Y3. The classifier Z3 is configured to return a numerical value (classification score) corresponding to the category data of the attribute Y3.

  Next, the classifier generation unit 32 increments the variable i by 1 in step S7, so that the variable i becomes 4. As a result, since the variable i is 4 and the total number of attributes N is 3, the classifier generation unit 32 determines that the variable i is greater than the total number of attributes N, and ends the process.

  As described above, when the total number of attributes of the input data 41 is N, N classifiers are generated by the processing of FIG. In this embodiment, since the input data 41 has three attributes Y1 to Y3, a classifier 42 having three classifiers Z1 to Z3 is generated by the classifier generation unit 32.

  Next, the process of the evaluation part 33 is demonstrated. FIG. 7 is a flowchart illustrating an example of the process of the evaluation unit 33, and FIG. 8 is a diagram illustrating an example of the classification score matrix 43 generated by the process of the evaluation unit 33.

  The evaluation unit 33 uses the input data 41 shown in FIG. 4 and the classifier 42 shown in FIG. 6 to generate a classification score matrix 43 shown in FIG. Note that the class shown in FIG. 4 may not be included in the input data in the evaluation unit 33. Further, the input data input to the evaluation unit 33 may be the input data 41 shown in FIG. 4 or other input data.

  In step S11, the evaluation unit 33 substitutes 1 for the variable i to obtain the total attribute number N and the total case number D. In the present embodiment, the total number of attributes N is 3, and the total number of cases D is 10.

  In step S12, the evaluation unit 33 substitutes 1 for the variable k, and in step S13, obtains the value a (k, i) of the case Xk and the attribute Yi. Here, since the variable k = 1 and the variable i = 1, the evaluation unit 33 obtains “A” as the value (category data) of the case X1 and the attribute Y1 of the input data 41.

  In step S14, the evaluation unit 33 obtains a classification score s (k, i) from the value a (k, i) using the classifier Zi corresponding to the attribute Yi. Here, using the classifier Z1 corresponding to the attribute Y1, “−0.146” is obtained as the classification score s (k, i) from the value “A” of the case X1 and the attribute Y1.

  In step S15, the evaluation unit 33 substitutes the classification score s (k, i) for the (k, i) component of the classification score matrix 43. As a result, “−0.146”, which is the classification score obtained in step S14, is assigned to the component of the case X1 and the attribute Y1 of the classification score matrix 43 shown in FIG.

  In step S16, the evaluation unit 33 increments the variable k by 1. In step S17, the evaluation unit 33 determines whether the variable k is greater than the total number of cases D.

  Here, since the variable k is 2 and the total number of cases D is 10, the evaluation unit 33 determines that the variable k is not greater than the total number of cases D, returns to step S13, and repeats the same processing. As a result, the classification score s (k, i) of the case X2, the attribute Y1, the classification score s (k, i) of the case X3, the attribute Y1,..., The classification score s (k, i) of the case X10, the attribute Y1. ) Is obtained and substituted into the classification score matrix 43.

  On the other hand, if the evaluation unit 33 determines in step S17 that the variable k is larger than the total number of cases D, the evaluation unit 33 proceeds to step S18. In step S18, the evaluation unit 33 increments the variable i by 1. In step S19, the evaluation unit 33 determines whether the variable i is greater than the total number of attributes N. Here, since the variable i is 2 and the total number of attributes N is 3, the evaluation unit 33 determines that the variable i is not greater than the total number of attributes N, returns to step S12, and repeats the same processing. As a result, the classification score s (k, i) of the case X1, the attribute Y2, the classification score s (k, i) of the case X2, the attribute Y2,..., The classification score s (k, i) of the case X10, the attribute Y2. ) Is obtained and substituted into the classification score matrix 43.

  By the same processing, when the variable i is incremented by 1 to 3, the classification score s (k, i) of the case X1, the attribute Y3, the classification score s (k, i) of the case X2, the attribute Y3,. The classification score s (k, i) of the case X10 and the attribute Y3 is obtained and substituted into the classification score matrix 43. On the other hand, when the variable i is incremented by 1 to 4, the evaluation unit 33 determines that the variable i is greater than the total number of attributes N, and ends the process.

  Through the above processing, the classification score matrix 43 shown in FIG. That is, by processing the input data 41 using the classifiers Z1 to Z3 corresponding to the attributes Y1 to Y3, the classification score matrix 43 into which the numerical values (classification scores) corresponding to the category data are substituted is generated. become.

  Next, the process of the inter-case similarity calculation unit 34 will be described. FIG. 9 is a flowchart illustrating an example of the process of the inter-case similarity calculation unit 34, and FIG. 10 illustrates an example of the inter-case similarity matrix 44 generated by the process of the inter-case similarity calculation unit 34. is there.

  The inter-case similarity calculation unit 34 generates the inter-case similarity matrix 44 shown in FIG. 10 using the classification score matrix 43 shown in FIG.

  In step S21, the inter-case similarity calculation unit 34 substitutes 1 for the variable i to obtain the total attribute number N and the total case number D. Here, the total number of attributes N is 3 and the total number of cases D is 10.

  In step S22, the inter-case similarity calculation unit 34 generates an inter-case similarity matrix 44 of a D-order square matrix and initializes all components with zero. Here, since the total number D of cases is 10, as shown in FIG. 10, a 10-row, 10-column case similarity matrix 44 is generated.

  In step S23, the inter-case similarity calculation unit 34 generates a row vector Vc (i) corresponding to the case Xi with the value of the case Xi and the attribute Yk as a (i, k). The variable k is 1, 2,..., N (total number of attributes). Here, a row vector Vc (i) corresponding to the case X1 is generated. Specifically, the inter-case similarity calculation unit 34 generates a row vector Vc (i) = (− 0.146, 0.176, 0.051) corresponding to the case X1 based on the classification score matrix 43. To do.

  The similarity calculation part 34 between cases substitutes i + 1 to the variable j in step S24. Here, since the variable i is 1, the variable j is 2.

  In step S25, the inter-case similarity calculation unit 34 generates a row vector Vc (j) corresponding to the case Xj with the value of the case Xj and the attribute Yk as a (j, k). Here, a row vector Vc (j) corresponding to case X2 is generated. Specifically, the inter-case similarity calculation unit 34, based on the classification score matrix 43, the row vector Vc (j) = (− 0.146, −0.250, −0.046) corresponding to the case X2. Is generated.

  In step S26, the inter-case similarity calculation unit 34 obtains a distance Dc (i, j) that decreases as the similarity between the row vector Vc (i) and the row vector Vc (j) increases. Dc (i, j) is substituted into the (i, j) component and (j, i) component of the inter-similarity matrix 44. Here, the distance Dc (1, 2) between the case X1 and the case X2 is substituted into the (1, 2) component and the (2, 1) component of the inter-case similarity matrix 44.

  As such a distance Dc (i, j), for example, the following (Formula 2) based on cosine similarity is used.

Dc (i, j) = 1− (Vc (i) · Vc (j)) / (| Vc (i) || Vc (j) |) (Expression 2)
The inter-case similarity calculation unit 34 increments the variable j by 1 in step S27, and determines whether or not the variable j is greater than the total number D of cases in step S28. Here, since the variable j is 3 and the total number of cases D is 10, the inter-case similarity calculation unit 34 determines that the variable j is not larger than the total number of cases D, returns to step S25, and performs the same processing. repeat.

  That is, the inter-case similarity calculation unit 34 obtains the distance Dc (1, 3) between the case X1 and the case X3, and the (1, 3) component and the (3, 1) component of the inter-case similarity matrix 44. Assign to. The similarity calculation part 34 between cases calculates | requires distance Dc (1,10) of the case X1 and the case X10 by repeating the same process, (1,10) component of the similarity matrix 44 between cases, and Substitute into the (10, 1) component.

  On the other hand, if the similarity calculation unit 34 determines that the variable j is larger than the total number of cases D, the variable i is incremented by 1 in step S29, and whether the variable i is larger than the total number of cases D in step S30. Determine whether or not. Here, since the variable i is 2 and the total number of cases D is 10, the inter-case similarity calculation unit 34 determines that the variable i is not larger than the total number of cases D, and returns to step S23. Repeat the process.

  That is, the similarity calculation part 34 between cases produces | generates the similarity matrix 44 between cases by performing the same process also about the case X2 to the case X10. And if the similarity calculation part 34 between cases determines with the variable i being larger than the total number D of cases, a process will be complete | finished.

  Through the above process, the inter-case similarity matrix 44 shown in FIG. The inter-case similarity matrix 44 indicates that the smaller the numerical value between the cases, the closer the distance between the cases (the cases are similar).

  Next, the process of the attribute similarity calculation unit 35 will be described. FIG. 11 is a flowchart illustrating an example of processing performed by the inter-attribute similarity calculation unit 35, and FIG. 12 illustrates an exemplary inter-attribute similarity matrix 45 generated by the processing performed by the inter-attribute similarity calculation unit 35. is there.

  The inter-attribute similarity calculation unit 35 generates the inter-attribute similarity matrix 45 shown in FIG. 12 using the classification score matrix 43 shown in FIG. In the process of FIG. 9, the distance between the cases is obtained using the row vector of the classification score matrix 43, but in the process of FIG. 11, the distance between the attributes is obtained using the column vector of the classification score matrix 43. ing.

  In step S41, the inter-attribute similarity calculation unit 35 substitutes 1 for the variable i to obtain the total attribute number N and the total case number D. Here, the total number of attributes N is 3 and the total number of cases D is 10.

  In step S42, the inter-attribute similarity calculation unit 35 generates an inter-attribute similarity matrix 45 of an Nth-order square matrix and initializes all components with zero. Here, since the total number of attributes N is 3, as shown in FIG. 12, an inter-attribute similarity matrix 45 of 3 rows and 3 columns is generated.

  In step S43, the inter-attribute similarity calculation unit 35 generates a column vector Va (i) corresponding to the attribute Yi with the value of the case Xk and the attribute Yi as a (k, i). The variable k is 1, 2,..., D (total number of cases). Here, a column vector Va (i) corresponding to the attribute Y1 is generated. Specifically, the inter-attribute similarity calculation unit 35, based on the classification score matrix 43, the column vector Va (i) = (− 0.146, −0.146, 0.222, 0 corresponding to the attribute Y1. 222, -0.146, -0.146, -0.146, -0.146, -0.146, 0.222).

  The inter-attribute similarity calculation unit 35 substitutes i + 1 for the variable j in step S44. Here, since the variable i is 1, the variable j is 2.

  In step S45, the inter-attribute similarity calculation unit 35 generates a column vector Va (j) corresponding to the attribute Yj with the value of the case Xk and the attribute Yj as a (k, j). Here, a column vector Va (j) corresponding to the attribute Y2 is generated. Specifically, the inter-attribute similarity calculation unit 35 determines, based on the classification score matrix 43, the column vector Va (j) = (0.176, −0.250, 0.051, 0,. 176, -0.250, 0.051, 0.000, 0.176, -0.250, -0.250).

  In step S46, the inter-attribute similarity calculation unit 35 obtains a distance Da (i, j) that decreases as the similarity between the column vector Va (i) and the column vector Va (j) increases. Da (i, j) is substituted into the (i, j) component and the (j, i) component of the inter-similarity matrix 45. Here, the distance Da (1, 2) between the attribute Y1 and the attribute Y2 is substituted into the (1, 2) component and the (2, 1) component of the inter-attribute similarity matrix 45.

  As such a distance Da (i, j), for example, the following (Formula 2) based on cosine similarity is used.

Da (i, j) = 1− (Va (i) · Va (j)) / (| Va (i) || Vc (j) |) (Equation 3)
The inter-attribute similarity calculation unit 35 increments the variable j by 1 in step S47, and determines whether or not the variable j is larger than the total number of attributes N in step S48. Here, since the variable j is 3 and the total number of attributes N is 3, the inter-attribute similarity calculation unit 35 determines that the variable j is not larger than the total number of attributes N, returns to step S45, and performs the same processing. repeat.

  That is, the inter-attribute similarity calculation unit 35 obtains the distance Da (1, 3) between the attribute Y1 and the attribute Y3, and the (1, 3) component and the (3, 1) component of the inter-attribute similarity matrix 45. Assign to.

  On the other hand, if the similarity calculation unit 35 determines that the variable j is larger than the total number of attributes N, the variable i is incremented by 1 in step S49, and whether the variable i is larger than the total number of attributes N in step S50. Determine whether or not. Here, since the variable i is 2 and the total number of attributes N is 3, the inter-attribute similarity calculation unit 35 determines that the variable i is not greater than the total number of attributes N, and returns to step S43. Repeat the process.

  That is, the inter-attribute similarity calculation unit 35 generates the inter-attribute similarity matrix 45 by performing the same process for the attribute Y2 and the attribute Y3. If the attribute similarity calculation unit 35 determines that the variable i is greater than the total number of attributes N, the process ends.

  Through the above processing, the attribute similarity matrix 45 shown in FIG. 12 is generated by the attribute similarity calculation unit 35. The inter-attribute similarity matrix 45 indicates that the smaller the numerical value between the attributes, the closer the distance between the attributes (the attributes are similar).

  As described above, the similarity calculation device 11 includes a plurality of cases and classifiers Z1 to Z3 that return numerical values based on likelihood for each attribute from the input data 41 in which each of the plurality of cases has a plurality of attribute values. Is applied to the input data 41, and the classification score matrix 43 is generated by substituting the numerical values returned from the classifiers Z1 to Z3. Then, the similarity calculation device 11 calculates an inter-case similarity matrix 44 and an attribute similarity matrix 45 from the classification score matrix 43. As a result, the similarity calculation device 11 can efficiently calculate the similarity between cases and the similarity between attributes from a plurality of cases and data in which each of the plurality of cases has a plurality of attribute values. Can be calculated automatically.

  The program for executing the operations described above is recorded or stored as a computer program product in its entirety or in part on a portable medium such as a flexible disk or CD-ROM or a storage medium such as a hard disk. Yes. The program code is read by a computer, and all or part of the operation is executed. Alternatively, all or part of the program can be distributed or provided via a communication network. The user can easily implement the present invention by downloading the program via a communication network and installing the program on a computer, or installing the program from a recording medium on the computer.

  In addition, each step in the flowcharts in the specification may be executed in a different order for each execution by changing the execution order and executing a plurality of steps at the same time, as long as it does not contradict its nature.

  Although several embodiments of the invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 1 ... Similarity calculation system, 11 ... Similarity calculation apparatus, 12 ... Database server, 21 ... Control part, 22 ... ROM, 23 ... RAM, 24 ... Input device, 25 ... Output device, 26 ... Communication IF, 27 ... Memory | storage Medium attachment / detachment unit, 28 ... bus, 31 ... similarity calculation processing unit, 32 ... classifier generation unit, 33 ... evaluation unit, 34 ... similarity calculation unit between cases, 35 ... similarity calculation unit between attributes, 41 ... input data , 42 ... classifier, 43 ... classification score matrix, 44 ... inter-case similarity matrix, 45 ... inter-attribute similarity matrix.

Claims (9)

  The ratio of the positive examples included in the class from input data including a plurality of cases and a set of a plurality of attributes and classes including positive examples and negative examples associated with each of the plurality of cases. And a classifier generator for generating a classifier for outputting a classification score from the category data for each attribute based on a ratio of the positive examples of predetermined category data included in the predetermined attribute. Similarity calculation device.   The similarity calculation apparatus according to claim 1, further comprising an evaluation unit that outputs the classification score based on the input data by the classifier and generates a classification score matrix.   The similarity according to claim 2, further comprising an inter-case similarity calculation unit that calculates an inter-case similarity based on the classification score of each attribute associated with each case from the classification score matrix. Computing device.   The attribute similarity calculation part which calculates the similarity between attributes based on the said classification score of each case matched with each attribute from the said classification score matrix, The Claim 2 or 3 characterized by the above-mentioned. Similarity calculation device.   The classifier generated by the classifier generator outputs a positive numerical value when the ratio of the positive examples included in the class is smaller than the ratio of the positive examples of the predetermined category data included in the predetermined attribute. When the ratio of the positive examples included in the class is larger than the ratio of the positive examples of the predetermined category data included in the predetermined attribute, a negative numerical value is output, and the positive examples included in the class are output. The similarity calculation apparatus according to claim 1, wherein when the proportion of an example is equal to the proportion of the positive example of the predetermined category data included in the predetermined attribute, 0 is output. 6. The similarity calculation apparatus according to claim 5, wherein the classifier generated by the classifier generation unit satisfies the following expression.
log ₁₀ P (i, j) −log ₁₀ Pt
Pt in the formula is a ratio of positive examples in all cases, and P (i, j) is a ratio of positive examples of predetermined category data j included in the predetermined attribute i.   The inter-case similarity calculation unit generates a row vector corresponding to each case from the classification score matrix, and the higher the similarity of the row vector corresponding to each case, the smaller the value becomes, The similarity calculation apparatus according to claim 3, wherein the similarity between cases is calculated.   The inter-attribute similarity calculation unit generates a column vector corresponding to each attribute from the classification score matrix, and obtains a distance that decreases as the similarity of the column vector corresponding to each attribute increases. The similarity calculation apparatus according to claim 4, wherein the similarity between attributes is calculated.   From the input data composed of a combination of a plurality of cases and a plurality of attributes and classes associated with each of the plurality of cases, the ratio of positive examples of the class in all cases and included in any attribute And generating a classifier that outputs a classification score from the category data for each of the attributes based on a percentage of positive examples of arbitrary category data.

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