BUILDING A CONCEPT HIERARCHY AUTOMATICALLY AND ITS MEASURING
Concept hierarchies are important for generalization in many data mining applications. Abundant algorithms have been proposed for automatic construction of concept hierarchy. A typical application of such algorithms is constructing directories for documents in information retrieval community. However, the research result can not be directly adopted for automatic construction of concept hierarchies for objects with identifiers only, such as items in market basket database where items have no attribute and only similarities between items are available. So, the metrics for directories for documents are not suitable for hierarchies for identifier-only data. In this paper, we propose a measurement that considers the unevenness of similarities among objects in the child nodes. We use the unevenness value to express the balance of concept hierarchies. For constructing a concept hierarchy, we propose a hierarchical clustering with join/merge decision (HCJMD) which is modified from hierarchical agglomerative clustering (HAC).
Concept hierarchy hierarchical agglomerative clustering data mining
HUANG-CHENG KUO HUNG-CHUNG LAI JEN-PENG HUANG
Department of Computer Science and Information Engineering National Chiayi University, Taiwan 600 Department of Information ManagementSouthern Taiwan University, Taiwan 710
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3975-3978
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）