TrackingDynamics

About

Tracking Dynamics of Topic Trends Using a Finite Mixture Model(KDD 2004)

TODO

• Initialize \mu in approprietly.

, and set \mu_i^{(0)} the first x_t s that are different each other.

Algorithm

Fitting Gaussian distribution in streaming documents. Parameters in step t are updated using parameters in step t-1.

Usage

make ttfmm  
./ttfmm input_file alpha lambda k_max  

• alpha[NUM]: Parameter of \alpha( > 0)
• lambda[NUM]: Parameter of \labmda(0 < lambda < 1)
• k_max[NUM]: # of maximum topic( > 0)

Format

Each line is one document.

timestamp \t word_1 \t word_2 \t ...  
timestamp \t word_1 \t word_2 \t ...  

Memo

• Small \lambda causes inf in \lambda^{-(t_{new} - t_{old})}.
• Dont calculate denominator in p(i|x_t) because |\Sigma| sometimes zero.
• Using logsumexp in p(i|x_t) to avoid overflow.