US 11,531,927 B2Granted

Categorical data transformation and clustering for machine learning using natural language processing

AssigneeAdobe Inc.

InventorsKourosh Modarresi, Abdurrahman Ibn Munir

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Abstract

Categorical data transformation and clustering techniques and systems are described for machine learning using natural language processing. These techniques and systems are configured to improve operation of a computing device to support efficient and accurate use of categorical data, which is not possible using conventional techniques. In an example, categorical data is received by a computing device that includes a categorical variable having a non-numerical data type for a number of classes. The categorical data is then converted into numerical data using natural language processing. Data is then generated by the computing device that includes a plurality of latent classes. This is performed by clustering the numerical data into a number of clusters that is smaller than the number of classes in the categorical data.

Claims20

Dependent claims

A method implemented by a computing device, the method comprising:

receiving, by the computing device, a plurality of categorical variables that, respectively, have a number of classes that are non-numerical;
responsive to determining, by the computing device, the number of classes in a first said categorical variable is less than a number, generating a first set of training data by converting the first said categorical variable into numerical data using natural language processing;
responsive to determining, by the computing device, the number of classes in a second said categorical variable is greater than the number, generating a second set of training data by:
converting the second said categorical variable into numerical data as vector representations using natural language processing;
generating a plurality of latent classes by clustering the vector representations of the numerical data from the second said categorical variable into a number of clusters having semantically similar said vector representations;
training, by the computing device, a model using machine learning based on:
the first set of training data including the numerical data converted from the first said categorical variable that have the number of classes that is less than the number;
and
the second set of training data including the plurality of latent classes formed by clustering the numerical data from the second said categorical variables that have the number of classes that is greater than the number;
processing, by the computing device,
data having the plurality of categorical variables using the trained model.

8 more elements

A computing device comprising:

a processing system;
and
a computer-readable storage medium storing instructions that, responsive to execution by the processing system, configures the processing system to perform operations comprising:
receiving a plurality of categorical variables that have, respectively a number of classes that are non-numerical;
responsive to determining that the number of classes in a first said categorical variable is less than a number, converting the first said categorical variable into numerical data using natural language processing to form a first set of training data;
responsive to determining that the number of classes in a second said categorical variable is greater than the number, generating a second set of training data by:
converting the second said categorical variable into numerical data using natural language processing;
and
generating a plurality of latent classes by clustering the numerical data of the second said categorical variable into a number of clusters that is smaller than the number of classes to form a second set of the training data;
training a model using machine learning using:
the first set of training data including the numerical data converted from the first said categorical variable that have the number of classes that is less than the number;
and
the second set of training data including the plurality of latent classes formed by clustering the numerical data from the second said categorical variable that have the number of classes that is greater than the number;
and
processing the plurality of categorical variables in subsequent data using the trained model.

12 more elements

One or more computer readable storage media storing instructions that, responsive to execution by a processing system, causes the processing system to perform operations comprising:

receiving a plurality of categorical variables having, respectively, a non-numerical data type having a number of classes;
responsive to determining that the number of classes in a first said categorical variable is less than a number, converting the first said categorical variable into numerical data using natural language processing to form a first set of training data;
responsive to determining that the number of classes in a second said categorical variable is greater than the number, generating a second set of training data by:
converting the second said categorical variable into numerical data using natural language processing;
and
generating a plurality of latent classes by clustering the numerical data from the second said categorical variable into a number of clusters that is smaller than the number of classes to form the second set of training data;
training a model using machine learning using:
the first set of training data including the numerical data converted from the first said categorical variable that has the number of classes that is less than the number;
and
the second set of training data including the plurality of latent classes formed by clustering the numerical data from the second said categorical variable that have the number of classes that is greater than the number;
and
processing subsequent data using the trained model.

9 more elements

Dependencies are inferred from the claim text.