Method of order-ranking document clusters using entropy data and bayesian self-organizing feature maps
First Claim
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1. A method of order-ranking document clusters in a plurality of web documents having keywords using entropy data and Bayesian SOM, said method comprising:
- a first step of recording a query word by a user;
a second step of designing a user profile made up of keywords used for most recent search and frequencies of the keywords, so as to reflect user'"'"'s preference;
a third step of calculating an entropy value between keywords of each web document and said query word and user profile;
a fourth step of collecting data and judging whether data for learning Kohonen neural network is sufficient or not;
a fifth step of ensuring a number of documents using a bootstrap algorithm statistical technique, if it is determined in said fourth step that said data for learning Kohonen neural network is not sufficient;
a sixth step of determining prior information to be used as an initial value for each of a network parameter through Bayesian learning, and determining an initial connection weight value of Bayesian SOM neural network model where said Kohonen neural network and Bayesian learning are coupled to one another; and
a seventh step of performing real-time document clustering for relevant documents of said plurality of web documents using said entropy value calculated in said third step and Bayesian SOM neural network model.
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Abstract
A method of order-ranking document clusters using entropy data and Bayesian self-organizing feature maps(SOM) is provided in which an accuracy of information retrieval is improved by adopting Bayesian SOM for performing a real-time document clustering for relevant documents in accordance with a degree of semantic similarity between entropy data extracted using entropy value and user profiles and query words given by a user, wherein the Bayesian SOM is a combination of Bayesian statistical technique and Kohonen network that is a type of an unsupervised learning.
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Citations
8 Claims
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1. A method of order-ranking document clusters in a plurality of web documents having keywords using entropy data and Bayesian SOM, said method comprising:
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a first step of recording a query word by a user;
a second step of designing a user profile made up of keywords used for most recent search and frequencies of the keywords, so as to reflect user'"'"'s preference;
a third step of calculating an entropy value between keywords of each web document and said query word and user profile;
a fourth step of collecting data and judging whether data for learning Kohonen neural network is sufficient or not;
a fifth step of ensuring a number of documents using a bootstrap algorithm statistical technique, if it is determined in said fourth step that said data for learning Kohonen neural network is not sufficient;
a sixth step of determining prior information to be used as an initial value for each of a network parameter through Bayesian learning, and determining an initial connection weight value of Bayesian SOM neural network model where said Kohonen neural network and Bayesian learning are coupled to one another; and
a seventh step of performing real-time document clustering for relevant documents of said plurality of web documents using said entropy value calculated in said third step and Bayesian SOM neural network model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification