22. The method of claim 2 , further comprising: defining, by at least one data processor, a product phrase as a logical product bundle across time, which comprises a consistent time-stamped sequence of products, wherein each product consistently co-occurs with all others in said product phrase with their relative time-lags, wherein a product phrase comprises: a product set containing a set of products in said phrase; and pair-wise time lags which contain time-lags between all product pairs; wherein time lags are measured in a time resolution unit which comprises any of days, weeks, months, quarters, or years depending on the application and retailer; a set of time-lag constraints where the time-lag between every pair of products in a phrase is the sum of the time lag between all successive products between them; and a slack parameter which determines how strictly product phrase time-lag constraints are imposed depending on how far said products are in said phrase.
22. The method of claim 2 , further comprising: defining, by at least one data processor, a product phrase as a logical product bundle across time, which comprises a consistent time-stamped sequence of products, wherein each product consistently co-occurs with all others in said product phrase with their relative time-lags, wherein a product phrase comprises: a product set containing a set of products in said phrase; and pair-wise time lags which contain time-lags between all product pairs; wherein time lags are measured in a time resolution unit which comprises any of days, weeks, months, quarters, or years depending on the application and retailer; a set of time-lag constraints where the time-lag between every pair of products in a phrase is the sum of the time lag between all successive products between them; and a slack parameter which determines how strictly product phrase time-lag constraints are imposed depending on how far said products are in said phrase. 23. The method of claim 22 , further comprising: obtaining, by at least one data processor, a fluid context by concatenating two-dimensional co-occurrence matrices along a time-lag dimension; wherein fluid context is created in the following steps: determining a co-occurrence count by using market basket and purchase sequence contexts to compute four counts for all time-lags; applying temporal smoothing; wherein all counts are smoothed using a low-pass filter or a smoothing kernels with different shapes that replace a raw count with a weighted average based on neighboring counts; and performing a consistency calculation; wherein said smoothed counts are then used to compute consistencies using a consistency measure.