Pattern-discovery and related algorithms

在不确定的环境中，使用来自多个来源的数据来识别重要的关系，对于在模式发现中发现新奇是非常重要的。例如，将基因型和表型属性联系起来，在理解生物学过程中起着至关重要的作用，在正常或新的条件下，涉及遗传修饰和变化，在诸如新生物产品工程的问题中。我们建议将生物过程作为一个信息系统来研究，包括识别生物分子的功能片段和分子位点之间的关系，并旨在了解它们对生物分子结构和功能的影响。我们研究的问题作为一个数据挖掘问题，使用粒度计算的范式，并提出分析方法的基础上不同类型的多值（多属性）关联模式提取的对齐数据。这些模式是基于对先前模型的统计测试的分析。目标是通过数据的选择、划分和属性聚类等提供非常灵活的分析/综合。结合层次和模块化分析的范式旨在建立一个比连续序列模式分析更一般化的模型，并且比多变量分析更具描述性。在检测到重要模式后，将开发进一步的模式挖掘方法来理解模式间的关系。

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.