Abstract
In many real-world learning problems the data flows continuously and learning algorithms should be able to respond to this circumstance: the induced concept description should gradually change over time. In this paper, we outline some existing incremental learners based on the theory of Formal Concept Analysis: FCA. Then, we introduce a new learning approach that improves incremental concept formation. This approach has the advantage of handling both the problem of data addition, data deletion, data update, attribute addition and attribute deletion. Finally, we apply the proposed approach to the problem of cancer diagnosis. We measure the effect of incrementality on the quality of the discovered rules using cross-validation.