引子    底线思维     无欲则刚   完备性  一致性   判定性   意识   分异   非理性

系统内命题真假只居其一，且命题真假可证，不存在真假无法判定

构造最大相容的集合之无穷势

挖掘最全模式的规律之大数据

产生最简无限的循环之置换术

建立最美封闭的组合之非线性

统一最真谐合的矛盾之演绎法

全息最善完备的仿生之仿真度

The self-category revolution of the expansion of consciousness ecology is a complex and multi-faceted concept, which involves the transformation and expansion of individuals' cognition, attitude, and paradigm towards the natural environment, social environment, and psychological environment. Consciousness ecology refers to people's cognition and attitude towards the natural environment, social environment, and psychological environment. It reflects humanity's understanding, interpretation, and approach to the world, and is of great significance to personal behavioral decision-making, social harmony and stability, as well as the sustainable development of the ecosystem. The self-category revolution of the expansion of consciousness ecology is a complex and multi-faceted process, which requires individuals to possess various qualities such as learning ability, reflective ability, and action ability. Through this process, individuals can better adapt to and promote social development, making positive contributions to the future of humanity.

In a logical system with incomplete rules, the existence of propositions that cannot be proven or disproved may lead to errors or inconsistencies in the reasoning process. Therefore, when constructing a logical system, it is necessary to minimize the incompleteness of the rules to reduce the difficulty and complexity of logical scale error correction. At the same time, logical scale error correction can help people detect and fix incompleteness in logical systems. Through careful examination and analysis of the logical system or reasoning process, people can identify potential rule incompleteness and take appropriate measures to fix it, thus enhancing the correctness and reliability of the logical system. In summary, rule incompleteness and logical scale error correction are two interrelated concepts. It is crucial to consider both aspects in the construction and utilization of logical systems to ensure their consistency and correctness.

PS: 

Pattern mining and discovery is a crucial process in data analysis, which involves extracting and identifying meaningful patterns, trends, or relationships from vast amounts of data. It utilizes data mining techniques and algorithms to uncover potential and valuable patterns or rules from complex and extensive datasets. These patterns or rules can manifest as frequent itemsets, association rules, trends, anomalies, and so forth.

The methodology and steps for pattern mining include data preprocessing, which involves cleaning, transforming, and standardizing raw data to eliminate noise, missing values, and outliers, thus improving data quality. Frequent itemset mining involves counting the frequency of each itemset and identifying those that occur more often than a predefined threshold. A frequent itemset refers to a set of items that appears at a higher frequency than a specified threshold. Association rule generation builds upon frequent itemsets by deriving association rules that meet a confidence requirement. An association rule is an implication expression in the form of X→Y, where X and Y are disjoint itemsets. Rule evaluation and filtering involve assessing and selecting the generated association rules that possess practical application value. Evaluation metrics include support, confidence, lift, and other relevant metrics.

附记   大数据与全息科学讲全息视域本源本体之大而无外小而无内说数据驱动与机器深度学习智能智慧化聊规律性挖掘

基底     划分      运算      映射    同构     转换    扩域

完备性     目的性    具象性    抽象性    应用性      规律性     逻辑性     对称性    一致性     非线性     确定性    稳定性     判定性    连续性    波动性    守恒性      封闭性     保守性     逼近性     离散化    量子化     数字化