Pattern-discovery and related algorithms

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Identifying significant relationships in an uncertain environment, using data from multiple sources, is extremely important in discovering novelty in pattern discovery. For example, linking genotype and phenotype attributes plays a vital role in understanding the biological processes, under normal or novel conditions involving genetic modifications and changes, in problems such as the engineering of novel bioproducts. We propose to study biological processes as an information system that includes identifying relationships of functional segments and molecular sites of biomolecules, and aim at understanding their effects on biomolecular structure and functionality. We study the problem as a data-mining problem using the paradigm of granular computing and propose analysis methods based on different types of multiple-value (multi-attribute) association patterns extracted from an alignment of data. The patterns are based on analysis of statistical testing from a prior model. The goal is to provide a very flexible analysis/synthesis through selection, partitioning and attribute clustering etc. of the data. The paradigm incorporating hierarchical and modular analysis aims at a more generalized model than contiguous sequential pattern analysis and is more descriptive than multiple-variable analysis. After detecting significant patterns, further pattern-mining methods to understand the inter-pattern relationships will be developed.