# US 2022/0044067 A1 · Data analysis apparatus and data analysis method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044067 A1 - Document ID: US20220044067A1 - Status: Published application - Kind: A1 - Application number: 17/155,443 - Filed: 2021-01-22 - Published: 2022-02-10 - Earliest claimed: 2020-08-05 - Assignees: Kioxia Corporation - Applicants: Kioxia Corporation - Inventors: Masahiro HAYASHI - Web page: https://patentgrep.com/patent/US20220044067A1 ## CPC - G06F 18/213: Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods - G06N 20/00: Machine learning - G06F 18/2163: {Partitioning the feature space} - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - H10P 74/23: {characterised by multiple measurements, corrections, marking or sorting processes} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H10P: GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10 ## Abstract A data analysis apparatus according to the present invention includes an extractor configured to extract, regarding a first feature amount among a plurality of feature amounts as an objective variable, a second feature amount having a linear relation or a nonlinear relation with the first feature amount as an explanatory variable by using analysis-target data that is sampled for the feature amounts. An adjuster is configured to set a node of base conversion for the second feature amount based on a significant difference between a linear regression result obtained by linear regression of the second feature amount and a nonlinear regression result obtained by nonlinear regression of the second feature amount. An analyzer is configured to divide the second feature amount based on the node set by the adjuster and perform regression analysis to generate a regression equation that represents the objective variable by the explanatory variable. ## Claims ### Claim 1 A data analysis apparatus comprising: an extractor configured to extract, regarding a first feature amount among a plurality of feature amounts as an objective variable, a second feature amount having a linear relation or a nonlinear relation with the first feature amount as an explanatory variable by using analysis-target data that is sampled for the feature amounts; an adjuster configured to set a node of base conversion for the second feature amount based on a significant difference between a linear regression result obtained by linear regression of the second feature amount and a nonlinear regression result obtained by nonlinear regression of the second feature amount; and an analyzer configured to divide the second feature amount based on the node set by the adjuster and perform regression analysis to generate a regression equation that represents the objective variable by the explanatory variable. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the extractor extracts the second feature amount based on indicator values each indicating a linear relation or a nonlinear relation of a corresponding one of the feature amounts with respect to the first feature amount. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein the extractor extracts the feature amount having the indicator value larger than a first threshold as the second feature amount, or extracts a predetermined number of the feature amounts in descending order of the indicator values as the second feature amounts. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the adjuster performs analysis of variance of the linear regression result and the nonlinear regression result to obtain the significant difference. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein the adjuster determines that the second feature amount is a linear explanatory variable having a linear relation with the first feature amount and sets no node of base conversion when there is no significant difference between the linear regression result and the nonlinear regression result, and determines that the second feature amount is a nonlinear explanatory variable having a nonlinear relation with the first feature amount and sets the node of base conversion for the second feature amount when there is the significant difference. ### Claim 6 (depends on 5) The apparatus of claim 5, wherein, when the second feature amount is a nonlinear explanatory variable, the adjuster sets number of the nodes based on a coefficient of determination obtained by regression analysis of the second feature amount. ### Claim 7 (depends on 6) The apparatus of claim 6, wherein the adjuster sets the number of the nodes when the coefficient of determination is maximum, as the number of the nodes for the second feature amount. ### Claim 8 (depends on 5) The apparatus of claim 5, wherein the adjuster determines a node position for the second feature amount based on a density of the analysis-target data of the second feature amount. ### Claim 9 (depends on 5) The apparatus of claim 5, wherein the adjuster determines a node position for the second feature amount in such a manner that the analysis-target data of the second feature amount is divided substantially equally. ### Claim 10 (depends on 5) The apparatus of claim 5, wherein the analyzer performs no division of the second feature amount when the second feature amount is a linear explanatory variable, and divides the second feature amount into parts, the number of which is in accordance with the node, and performs regression analysis when the second feature amount is a nonlinear explanatory variable. ### Claim 11 (depends on 1) The apparatus of claim 1, wherein the extractor, the adjuster, and the analyzer are configured in an arithmetic processor, and the apparatus further comprises: a memory configured to store therein a program that causes the arithmetic processor to perform the regression analysis; and a database configured to store the analysis-target data therein. ### Claim 12 (depends on 1) The apparatus of claim 1, further comprising a display configured to display the explanatory variable related to the objective variable and a regression coefficient of the explanatory variable by regression analysis by the analyzer. ### Claim 13 A data analysis method using a data analysis apparatus including an arithmetic processor, the method comprising: extracting, regarding a first feature amount among a plurality of feature amounts as an objective variable, a second feature amount having a linear relation or a nonlinear relation with the first feature amount as an explanatory variable by using analysis-target data that is sampled for the feature amounts; determining whether the second feature amount has a linear relation or a nonlinear relation with the first feature amount, and setting a node of base conversion for the second feature amount when the second feature amount has a nonlinear relation with the first feature amount; and dividing the second feature amount based on the node and performing regression analysis to generate a regression equation that represents the objective variable by the explanatory variable. ### Claim 14 (depends on 13) The method of claim 13, wherein the second feature amount is extracted based on indicator values each indicating a linear relation or a nonlinear relation of a corresponding one of the feature amounts with respect to the first feature amount. ### Claim 15 (depends on 14) The method of claim 14, wherein the feature amount having the indicator value larger than a first threshold is extracted as the second feature amount, or a predetermined number of the feature amounts are extracted in descending order of the indicator values as the second feature amounts. ### Claim 16 (depends on 15) The method of claim 15, wherein it is determined whether the second feature amount has a linear relation or a nonlinear relation with the first feature amount based on a significant difference between a linear regression result obtained by linear regression of the second feature amount and a nonlinear regression result obtained by nonlinear regression of the second feature amount. ### Claim 17 (depends on 16) The method of claim 16, wherein the second feature amount is determined as a linear explanatory variable having a linear relation with the first feature amount and no node of base conversion is set, when there is no significant difference between the linear regression result and the nonlinear regression result, and the second feature amount is determined as a nonlinear explanatory variable having a nonlinear relation with the first feature amount and the node of base conversion is set for the second feature amount, when there is the significant difference. ### Claim 18 (depends on 17) The method of claim 17, wherein, when the second feature amount is a nonlinear explanatory variable, number of the nodes is set based on a coefficient of determination obtained by regression analysis of the second feature amount. ### Claim 19 (depends on 18) The method of claim 18, wherein the number of the nodes when the coefficient of determination is maximum is set as the number of the nodes for the second feature amount. ### Claim 20 (depends on 17) The method of claim 17, wherein no division is performed for the second feature amount that is a linear explanatory variable, and division into parts, number of which is in accordance with the node, and regression analysis are performed for the second feature amount that is a nonlinear explanatory variable.