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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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