Data Cleaning publication categorizer

Recent advancement in biotechnology has produced a massive
amount of raw biological data which are accumulating at an
exponential rate. Errors, redundancy and discrepancies are
prevalent in the raw data, and there is a serious need for
systematic approaches towards biological data cleaning. This
work examines the extent of redundancy in biological data and
proposes a method for detecting duplicates in biological data.
Duplicate relations in a real-world biological dataset are modeled
into forms of association rules so that these duplicate relations or