Embedded Event and Trend Diagnostics to Extract LDA Topic Models on Real Time Multi-Data Streams

Existing Latent Dirichlet Allocation (LDA) methods make use of random mixtures over latent topics and each topic is characterized by a distribution over words from both batch and continuous streams over time. However, it is nontrivial to explore the correlation with the existence of different among multiple data streams, i.e., documents from different multiple data streams about the same topic may have different time stamps. This paper introduces a new novel algorithm based on the Latent Dirichlet Allocation (LDA) topic model.

Provided by: WSEAS Topic: Data Management Date Added: Dec 2012 Format: PDF

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