The invention provides a user group classification method, a storage medium and a terminal, and aims to solve the problem of poor usage efficiency of user groups in the prior art. The method comprisesthe steps of obtaining friend-making relationships and social behavior data among users, and user attributes of the users; by taking the users as points and the friend-making relationships among theusers as edges, establishing a first friend-making relationship network; based on the user attributes of the users, performing value assignment on the points in the first friend-making relationship network, and based on the social behavior data among the users, performing value assignment on the edges in the first friend-making relationship network, thereby obtaining a second friend-making relationship network; and based on the second friend-making relationship network, classifying the users to obtain the user groups which the users belong to. Therefore, the usage efficiency of the user groupsis improved.

User group classification method, storage medium and terminal

The invention discloses a prediction method of abnormal measurement data of a transformer substation. The method comprises steps of data extraction, data analysis, data cleaning, attribute construction, abnormal measurement prediction model, stability checking, white noise detection, model identification, model checking, model evaluation, and measurement data detection by using the model after model evaluation standard satisfaction. According to the invention, abnormal measurement prediction is carried out by using a time sequence analysis method, a K-mean cluster analysis method and a maximumlikelihood ratio method; historical data and real-time data are selected from data sources; K-mean cluster analysis is carried out on a typical value of substation measurement; an extracted value periodicity is analyzed; after data cleaning and data transformation, data maturization is carried out to form data for modeling; the modeling data are constructed by using the time sequence analysis method and measurement data of the substation are predicted by using the model; and the predicted data are compared with a substation measurement standard value and comparison and determination are carried out in a normal range, and then the comparison result is displayed vividly.

Prediction method of abnormal measurement data of transformer substation

The invention discloses a medical consuming client automatic classification method based on K-means. The method comprises the steps that S1, data is extracted from a client database through counting;S2, a client classification model is constructed according to the annual consuming frequency and annual consuming total amount of clients based on a CRM (Customer Relationship Management) method; S3,feature vectors of each client are obtained by using the client classification model in step S2; S4, aggregation is conducted on the obtained feature vectors in step S3 by using a machine learning K-means algorithm; S5, an aggregation result obtained in step S4 is taken as a client classification result to achieve the classification of the clients. According to the method, a related algorithm of machine learning is used to conduct automatic classification on the clients according to the consuming frequency and consuming total amount of the clients, the classification on the clients including classification standards is all completed by machines, and manpower and material resources are saved.

Medical consuming client automatic classification method based on K-means

The invention discloses a customer classification method and system, and relates to the field of customer classification, and the method comprises the steps: obtaining historical data; generating a corresponding feature dimension data table according to the historical data and a preset feature dimension index, the feature dimension data table comprising feature dimension data of a plurality of customers; establishing an analysis sample pool according to the feature dimension data table; carrying out dimension reduction on the feature dimension data in the analysis sample pool by adopting a factor analysis algorithm to obtain a corresponding factor score data table; and according to the factor score data table, classifying the customers by adopting a clustering algorithm. The dimensionalityreduction effect of the factor analysis algorithm and the clustering algorithm are adopted for classification at the same time, the limitation that the number of feature dimensionalities can be considered in a conventional customer classification method is broken through, and the two algorithms are combined for use, so that the precision and the accuracy of the classification result are improved.

A customer classification method and system

The invention discloses a user value classification method and device based on clustering analysis, and relates to the technical field of computers. The method specifically comprises the steps of determining an original feature vector of each user according to an index value under each index feature of the user; conducting factor analysis on the original feature vector of each user, and regarding public factors of which the factor scores satisfies a set rule as factor variables; determining a comprehensive feature vector of each user based on the value of each user under the factor variables; conducting clustering analysis on the comprehensive feature vector of each user to determine the classification of each user. According to the embodiment, index features can be prevented from being excessively complicated, the number of factors for clustering analysis is lowered, and the time cost and the database resource consumption amount for user value classification are reduced; the situation that the weight is given to each index feature according to experience can be avoided, the weight allocation accuracy is improved, and the accuracy of user value classification is then improved; the objectivity and the accuracy for user value grouping can be improved.

User value classification method and device based on clustering analysis

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User value classification method and device based on clustering analysis