用无监督认知模型挖掘数据中的关键模式与特征。
Knowledge Discovery using Unsupervised Cognition
- 基于已训练模型进行模式挖掘与特征筛选
- 在知识发现任务上超越现有方法表现
- 适合需要解释性分析的数据探索场景
知识发现对于理解数据集及其组件间潜在关系至关重要。本文提出一种名为无监督认知（Unsupervised Cognition）的新颖无监督学习算法，专注于建模已学习数据。本研究提出三种技术，用于对已训练的无监督认知模型进行知识发现：一是模式挖掘技术，二是基于该技术的特征选择方法，三是基于特征选择的降维技术。最终目标是区分相关与无关特征，并构建可提取有意义模式的模型。通过实证实验验证，所提方法在知识发现任务中优于当前最优水平。
原文摘要 · Abstract (English)
Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to perform knowledge discovery over an already trained Unsupervised Cognition model. Specifically, we present a technique for pattern mining, a technique for feature selection based on the previous pattern mining technique, and a technique for dimensionality reduction based on the previous feature selection technique. The final goal is to distinguish between relevant and irrelevant features and use them to build a model from which to extract meaningful patterns. We evaluated our proposals with empirical experiments and found that they overcome the state-of-the-art in knowledge discovery.
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