Method and system for detection, classification and prediction of user behavior trends

Abstract

A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The method and system reduces the n-dimensional feature space to lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

What is claimed is: 
     
         1 . A method for detection of user behaviour trends, wherein the method comprises of
 performing pre-processing and feature selection on raw data by a cluster master, wherein the raw data comprises of data related to temporal behaviour of a user;   obtaining trend data from the raw data by the cluster master;   reducing dimensionality of the raw data by the cluster master to a lower dimension using correspondence analysis, wherein the data with the lower dimension causes users with similar behaviour to be closer to each other than those who are dissimilar;   performing clustering on the data with the lower dimension by the cluster master based on attributes of the user; and   assigning at least one label to the clustered data by the cluster master.   
     
     
         2 . The method, as claimed in  claim 1 , wherein pre-processing and feature selection on the raw data comprises determining the attributes of the users. 
     
     
         3 . The method, as claimed in  claim 1 , wherein trend data comprises behaviour that changes over time. 
     
     
         4 . The method, as claimed in  claim 1 , wherein assigning at least one label to the clustered data is based on label information of previous users according to actions taken by the users previously. 
     
     
         5 . The method, as claimed in  claim 1 , wherein the method further comprises of predicting future actions of the users based on the labeled clustered data. 
     
     
         6 . The method, as claimed in  claim 5 , wherein the method further comprises of augmenting the predictions of future actions by generating confidence measures based on class membership proportional to the labeled clustered data. 
     
     
         7 . The method, as claimed in  claim 1 , wherein the method further comprises of
 applying association rule mining on the clustered data to discover at least one rule; and   using the at least one discovered rule for user targeted applications.   
     
     
         8 . The method, as claimed in  claim 7 , wherein applying association rule mining on the clustered data to discover at least one rule comprises of
 finding relationships between features of users in a cluster features of users who were previously converted by historical campaigns and features of previous campaigns themselves;   mining underlying rules in the clustered data; and   discovering defining attributes of each campaign, relationship of attributes of each campaign, other attributes of the campaign and previously converted users.   
     
     
         9 . The method, as claimed in  claim 1 , wherein the method further comprises of detecting unusual events based on the raw data. 
     
     
         10 . The method, as claimed in  claim 1 , wherein the raw data is at least one of numerical multinomial data; and an array having n-dimensions, where the raw data comprises of continuous individual features. 
     
     
         11 . A computer program product comprising computer executable program code recorded on a computer readable non-transitory storage medium, said computer executable program code when executed, causing a method for detection, classification and prediction of user behaviour trends, comprising:
 performing pre-processing and feature selection on raw data, wherein the raw data comprises of data related to temporal behaviour of a user;   obtaining trend data from the raw data;   reducing dimensionality of the raw data to a lower dimension using correspondence analysis, wherein the data with the lower dimension causes users with similar behaviour to be closer to each other than those who are dissimilar;   performing clustering on the data with the lower dimension based on attributes of the user; and   assigning at least one label to the clustered data.   
     
     
         12 . The computer program product, as claimed in  claim 11 , wherein pre-processing and feature selection on the raw data comprises determining the attributes of the users. 
     
     
         13 . The computer program product, as claimed in  claim 11 , wherein trend data comprises behaviour that changes over time. 
     
     
         14 . The computer program product, as claimed in  claim 11 , wherein assigning at least one label to the clustered data is based on label information of previous users according to actions taken by the users previously. 
     
     
         15 . The computer program product, as claimed in  claim 11 , wherein the method further comprises of predicting future actions of the users based on the labeled clustered data. 
     
     
         16 . The computer program product, as claimed in  claim 15 , wherein the method further comprises of augmenting the predictions of future actions by generating confidence measures based on class membership proportional to the labeled clustered data. 
     
     
         17 . The computer program product, as claimed in  claim 11 , wherein the method further comprises of
 applying association rule mining on the clustered data to discover at least one rule; and   using the at least one discovered rule for user targeted applications.   
     
     
         18 . The computer program product, as claimed in  claim 17 , wherein applying association rule mining on the clustered data to discover at least one rule comprises of
 finding relationships between features of users in a cluster features of users who were previously converted by historical campaigns and features of previous campaigns themselves;   mining underlying rules in the clustered data; and   discovering defining attributes of each campaign, relationship of attributes of each campaign, other attributes of the campaign and previously converted users.   
     
     
         19 . The computer program product, as claimed in  claim 11 , wherein the method further comprises of detecting unusual events based on the raw data. 
     
     
         20 . The computer program product, as claimed in  claim 11 , wherein the raw data is at least one of numerical multinomial data; and an array having n-dimensions, where the raw data comprises of continuous individual features.