一种基于概念的数据聚类模型

在数据挖掘研究领域,现有的大多数聚类算法都受到数据可伸缩性和结果可解释性的限制.为了解决这一难题,提出了一种基于概念的数据聚类模型.该模型从描述数据样本的数据本身出发,首先在预处理后的数据集上提取基本概念,再对这些概念进行概化,形成表示聚类结果的高层概念,最后基于这些高层概念进行样本划分,从而完成整个聚类过程.该模型能够在保证聚类准确性的基础上,很大程度地减少要处理的数据量,提高原算法的可伸缩性.另外,该模型基于概念进行知识的发现与分析,能够提高聚类结果的可解释性,便于与用户交互.实验结果表明,该模型对于聚类结果较好且复杂度较高的算法尤为有效.

Concept-Based Data Clustering Model

In data mining, lots of clustering algorithms have been developed, and most of them are limited by scalability and interpretability. To solve this problem, a concept-based data clustering model is presented. From the perspective of the metadata describing samples, some basic concepts are extracted from the preprocessed dataset firstly in this model, and then generalizes, higher level concepts representing clustering results. Finally, the samples are classified into different final concepts and the clustering process is completed. On the premise of ensuring the accuracy of the clustering results, this model can greatly decrease the number of tuples needing to be processed, improving the data scalability of clustering algorithms. In addition, to discover and analyze knowledge based on concepts, this model can improve the interpretability of clustering results, and facilitate to interact with users. Experimental results show that the proposed model is more useful to the algorithms with higher computation cost and better results.