On mining complex sequential data by means of FCA and pattern structures
This work addresses the challenge of analyzing heterogeneous sequential data for applications like healthcare, though it appears incremental as it builds on existing FCA and pattern structure methods.
The authors tackled the problem of mining complex sequential data by using Formal Concept Analysis (FCA) and pattern structures to discover interesting sequential patterns, showing improved pattern meaningfulness and computing efficiency in a healthcare dataset on cancer.
Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work. Keywords: data mining; formal concept analysis; pattern structures; projections; sequences; sequential data.