Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.

Abstract: The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Abstract: Embodiments of the disclosure provide a text information clustering method and a text information clustering system. The method can include performing word segmentation on multiple pieces of text information to generate multiple words; performing an initial clustering on the multiple words to generate multiple first-level topics, each of the first-level topics comprising at least two pieces of text information; determining, for each of the first-level topics, a number of second-level topics based on a number of pieces of text information under the first-level topic; and performing, according to the number of second-level topics of each of the first-level topics, a secondary clustering on the multiple words of at least two pieces of text information comprised in the first-level topic to generate multiple second-level topics.

Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.

Abstract: The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.