  Novelty detection in news events has long been a difficult problem. A number
of models performed well on specific data streams but certain issues are far
from being solved, particularly in large data streams from the WWW where
unpredictability of new terms requires adaptation in the vector space model. We
present a novel event detection system based on the Incremental Term
Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with
Locality Sensitive Hashing (LSH). Our system could efficiently and effectively
adapt to the changes within the data streams of any new terms with continual
updates to the vector space model. Regarding miss probability, our proposed
novelty detection framework outperforms a recognised baseline system by
approximately 16% when evaluating a benchmark dataset from Google News.
