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In dieser Arbeit wird eine Kombinationsmöglichkeit von fallbasiertem und induktivem Schliessen, basierend auf k-d- und Entscheidungsbäumen, entwickelt. Dabei wurde versucht, die Vorteile des induktiven Mechanismus, wie z. B. die sehr effiziente Klassifiz ierung und automatische Generierung, in den fallbasierten Mechanismus zu integrieren. Die Aufgabe zerfällt dabei in zwei Teilaufgaben, die im folgenden zusammengefasst werden.
Retrieval of cases is one important step within the case-based reasoning paradigm. We propose an improvement of this stage in the process model for finding most similar cases with an average effort of O[log2n], n number of cases. The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k-d tree, for efficient case retrieval according to a given similarity measure sim. In addition to illustrating the basic idea, we present the expe- rimental results of a comparison of four different k-d tree generating strategies as well as introduce the notion of virtual bounds as a new one that significantly reduces the retrieval effort from a more pragmatic perspective. The presented approach is fully implemented within the (Patdex) system, a case-based reasoning system for diagnostic applications in engineering domains.