Capturing truthiness: mining truth tables in binary datasets

We introduce a new data mining problem: mining truth tables in binary datasets. Given a matrix of objects and the properties they satisfy, a truth table identifies a subset of properties that exhibit maximal variability (and hence, complete independence) in occurrence patterns over the underlying objects. This problem is relevant in many domains, e.g., in bioinformatics where we seek to identify and model independent components of combinatorial regulatory pathways, and in social/economic demographics where we desire to determine independent behavioral attributes of populations. We outline a family of levelwise approaches adapted to mining truth tables, algorithmic optimizations, and applications to bioinformatics and political datasets. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications - Data Mining; I.2.6 [Artificial Intelligence]: Learning General Terms: Algorithms.

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Clifford Conley Owens III, T. M. Murali, Naren Ramakrishnan
• Keywords: Applied Computing, Data Mining, Mining Truth Tables, SAC 2009, Truth Tables