Double Clustering Based Neural Feedback Method for Unstructured Text Data

Abstract

Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.