  Event datasets in the financial domain are often constructed based on actual
application scenarios, and their event types are weakly reusable due to
scenario constraints; at the same time, the massive and diverse new financial
big data cannot be limited to the event types defined for specific scenarios.
This limitation of a small number of event types does not meet our research
needs for more complex tasks such as the prediction of major financial events
and the analysis of the ripple effects of financial events. In this paper, a
three-stage approach is proposed to accomplish incremental discovery of event
types. For an existing annotated financial event dataset, the three-stage
approach consists of: for a set of financial event data with a mixture of
original and unknown event types, a semi-supervised deep clustering model with
anomaly detection is first applied to classify the data into normal and
abnormal events, where abnormal events are events that do not belong to known
types; then normal events are tagged with appropriate event types and abnormal
events are reasonably clustered. Finally, a cluster keyword extraction method
is used to recommend the type names of events for the new event clusters, thus
incrementally discovering new event types. The proposed method is effective in
the incremental discovery of new event types on real data sets.
