Interval graph mining

Abstract

Frequent subgraph mining is a difficult data mining problem aiming to find the exact set of frequent subgraphs into a database of graphs. Current subgraph mining approaches make use of the canonical encoding which is one of the key operations. It is well known that canonical encodings have an exponential time complexity. Consequently, mining all frequent patterns for large and dense graphs is computationally expensive. In this paper, we propose an interval approach to handle canonicity, leading to two encodings, lower and upper encodings, with a polynomial time complexity, allowing to tightly enclose the exact set of frequent subgraphs. These two encodings lead to an interval graph mining algorithm where two minings are launched in parallel, a lower mining (resp. upper mining) using the lower (resp. upper) encoding. The interval graph mining approach has been implemented within the state of the art Gaston miner. Experiments performed on synthetic and real graph databases coming from stock market and biological datasets show that our interval graph mining is effective on dense graphs.