Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
IEEE Transactions on Knowledge and Data Engineering
An extensive study on automated Dewey Decimal Classification
Journal of the American Society for Information Science and Technology
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In an interactive classification application, a user may find it more valuable to develop a diagnostic decision support method which can reveal significant classification behavior of exemplar records. Such an approach has the additional advantage of being able to optimize the decision process for the individual record in order to design more effective classification methods. In this paper, we propose the Subspace Decision Path method which provides the user with the ability to interactively explore a small number of nodes of a hierarchical decision process so that the most significant classification characteristics for a given test instance are revealed. In addition, the SD-Path method can provide enormous interpretability by constructing views of the data in which the different classes are clearly separated out. Even in cases where the classification behavior of the test instance is ambiguous, the SD-Path method provides a diagnostic understanding of the characteristics which result in this ambiguity. Therefore, this method combines the abilities of the human and the computer in creating an effective diagnostic tool for instance-centered high dimensional classification.